Compare commits
48 changed files with 9 additions and 1776 deletions
2
.gitignore
vendored
2
.gitignore
vendored
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__pycache__/
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competition/.venv-competition/
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2
Justfile
2
Justfile
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vendor:
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pip download -r competition/requirements-vendor.txt -d competition/wheelhouse/ --python-version 3.12 --platform manylinux --no-deps
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#!/usr/bin/env bash
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# One-time intake step: install the vendored wheels offline.
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# The sandbox has no network, so everything must come from wheelhouse/.
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set -euo pipefail
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cd "$(dirname "$0")"
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python -m pip install --no-index --find-links=wheelhouse -r requirements-vendor.txt
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echo "[build] vendored wheels installed" >&2
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"""Competition stdio agent: direct policy sampling over a trained equinox checkpoint.
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Self-contained competition bot. It speaks the wire protocol in
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`generals-bots/competition/protocol.py` (handshake, then one observation frame
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per turn, one action line per turn, EOF on stdin = game over) and, on each
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turn, samples an action from the policy network exactly as the model is used
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during training/evaluation — no tree search.
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Unlike `scripts/mcts_agent.py`, this module does **not** import the
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`general_bots_training` training package. It inlines the policy-value network
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and the observation encoding so the script can be dropped into the competition
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sandbox with just the engine (`generals`), `equinox`, `jax`, and `numpy`
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available.
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This is the search-free counterpart to `scripts/competition_puct.py`: it shares
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the same network and observation encoding, but selects an action by sampling
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from the masked policy (as in `PolicyValueNetwork.__call__`) instead of running
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particle PUCT.
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Run it directly through the bundled matchup driver, e.g.:
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PYTHONPATH=src:generals-bots .venv/bin/python \
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generals-bots/competition/matchup.py \
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scripts/competition.py --checkpoint ppo_model.eqx \
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generals-bots/competition/agents/expander_python/run.sh \
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--mode competition
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or wrap it in a `run.sh` that passes the checkpoint path.
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"""
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import argparse
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import os
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import sys
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import time
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import equinox as eqx
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import jax
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import jax.numpy as jnp
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import jax.random as jrandom
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import numpy as np
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from generals.core import game
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from generals.core.action import compute_valid_move_mask
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from generals.core.game import GameState
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from generals.core.observation import Observation
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from generals.modifiers import build_castles, deathtouch
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os.environ.setdefault("JAX_PLATFORMS", "cpu")
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PASS_ACTION = np.array([1, 0, 0, 0, 0], dtype=np.int32)
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# ---------------------------------------------------------------------------
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# Policy-value network (inlined from general_bots_training.network)
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# ---------------------------------------------------------------------------
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def obs_to_tensor(obs: Observation) -> jnp.ndarray:
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"""Encode an Observation into a (C, H, W) float32 tensor for the network.
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Armies and army-counts are log-normalized; land counts are divided by the
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number of cells; scalar values are broadcast to spatial planes so a plain
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conv stack can consume them. Grid-size agnostic.
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"""
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H, W = obs.armies.shape
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armies = jnp.log1p(obs.armies.astype(jnp.float32)) / jnp.log(50.0)
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def bcast(scalar):
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return jnp.broadcast_to(scalar.astype(jnp.float32), (H, W))
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own_land = bcast(obs.owned_land_count / (H * W))
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own_army = bcast(jnp.log1p(obs.owned_army_count.astype(jnp.float32)) / jnp.log(50.0))
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opp_land = bcast(obs.opponent_land_count / (H * W))
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opp_army = bcast(jnp.log1p(obs.opponent_army_count.astype(jnp.float32)) / jnp.log(50.0))
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timestep = bcast(jnp.log1p(obs.timestep.astype(jnp.float32)) / jnp.log(1201.0))
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return jnp.stack(
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[
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armies,
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obs.generals.astype(jnp.float32),
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obs.castles.astype(jnp.float32),
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obs.mountains.astype(jnp.float32),
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obs.neutral_cells.astype(jnp.float32),
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obs.owned_cells.astype(jnp.float32),
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obs.opponent_cells.astype(jnp.float32),
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obs.fog_cells.astype(jnp.float32),
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obs.structures_in_fog.astype(jnp.float32),
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own_land,
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own_army,
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opp_land,
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opp_army,
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timestep,
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],
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axis=0,
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)
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class PolicyValueNetwork(eqx.Module):
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"""Conv policy-value network.
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Action layout: 4 full-move directions and 4 half-move (split) directions
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per source cell, plus one global pass action. Invalid moves are masked to
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-1e9; pass is always available.
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The value head uses global average pooling over the spatial grid, so the
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network works for any board size without reshaping linear layers.
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"""
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conv1: eqx.nn.Conv2d
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conv2: eqx.nn.Conv2d
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conv3: eqx.nn.Conv2d
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conv4: eqx.nn.Conv2d
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policy_conv: eqx.nn.Conv2d
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value_conv: eqx.nn.Conv2d
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value_linear1: eqx.nn.Linear
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value_linear2: eqx.nn.Linear
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def __init__(self, key, in_channels: int = 14, channels=(32, 32, 32, 16)):
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keys = jrandom.split(key, 8)
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self.conv1 = eqx.nn.Conv2d(in_channels, channels[0], kernel_size=3, padding=1, key=keys[0])
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self.conv2 = eqx.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1, key=keys[1])
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self.conv3 = eqx.nn.Conv2d(channels[1], channels[2], kernel_size=3, padding=1, key=keys[2])
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self.conv4 = eqx.nn.Conv2d(channels[2], channels[3], kernel_size=3, padding=1, key=keys[3])
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# 9 = 4 dirs (full) + 4 dirs (half) + 1 pass
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self.policy_conv = eqx.nn.Conv2d(channels[3], 9, kernel_size=1, key=keys[4])
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self.value_conv = eqx.nn.Conv2d(channels[3], 4, kernel_size=1, key=keys[5])
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self.value_linear1 = eqx.nn.Linear(4, 64, key=keys[6])
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self.value_linear2 = eqx.nn.Linear(64, 1, key=keys[7])
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def _features(self, obs):
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x = jax.nn.relu(self.conv1(obs))
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x = jax.nn.relu(self.conv2(x))
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x = jax.nn.relu(self.conv3(x))
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x = jax.nn.relu(self.conv4(x))
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return x
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def _value_from_features(self, feat):
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v = jax.nn.relu(self.value_conv(feat)) # (4, H, W)
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v = v.mean(axis=(1, 2)) # (4,) global average pool
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v = jax.nn.relu(self.value_linear1(v))
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return self.value_linear2(v)[0]
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def policy_value(self, obs, mask):
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"""Return deterministic masked policy logits and the critic value."""
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features = self._features(obs)
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return self._policy_logits(features, mask), self._value_from_features(features)
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def _policy_logits(self, feat, mask):
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"""Return 8*H*W move logits plus one global pass logit."""
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logits = self.policy_conv(feat) # (9, H, W)
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mask_t = jnp.transpose(mask, (2, 0, 1)) # (4, H, W)
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penalty = (1.0 - mask_t) * -1e9
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move_penalty = jnp.concatenate([penalty, penalty], axis=0)
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move_logits = (logits[:8] + move_penalty).reshape(-1)
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pass_logit = jnp.mean(logits[8])[None]
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return jnp.concatenate([move_logits, pass_logit])
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# ---------------------------------------------------------------------------
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# Action selection (matches training/evaluation rollout)
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# ---------------------------------------------------------------------------
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@jax.jit
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def competition_move_mask(obs: Observation) -> jnp.ndarray:
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"""Legal move mask, identical to the one used during training/evaluation."""
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return compute_valid_move_mask(obs.armies, obs.owned_cells, obs.mountains)
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def _decode_policy_index(index: int, height: int, width: int) -> np.ndarray:
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cells = height * width
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if index == 8 * cells:
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return PASS_ACTION.copy()
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encoded_direction, position = divmod(index, cells)
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row, col = divmod(position, width)
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split = int(encoded_direction >= 4)
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direction = encoded_direction - 4 if split else encoded_direction
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return np.array([0, row, col, direction, split], dtype=np.int32)
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def observation_from_wire(
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timestep: int,
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owned_land_count: int,
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owned_army_count: int,
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opponent_land_count: int,
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opponent_army_count: int,
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type_grid,
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owner_grid,
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army_grid,
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) -> Observation:
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"""Convert a competition wire frame into the network's Observation type."""
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types = jnp.asarray(type_grid, dtype=jnp.int32)
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owners = jnp.asarray(owner_grid, dtype=jnp.int32)
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armies = jnp.asarray(army_grid, dtype=jnp.int32)
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visible = (types != 0) & (types != 5)
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return Observation(
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armies=armies,
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generals=types == 4,
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castles=types == 3,
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mountains=types == 2,
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neutral_cells=visible & (owners == 0) & (types != 2),
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owned_cells=owners == 1,
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opponent_cells=owners == 2,
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fog_cells=types == 0,
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structures_in_fog=types == 5,
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owned_land_count=jnp.int32(owned_land_count),
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owned_army_count=jnp.int32(owned_army_count),
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opponent_land_count=jnp.int32(opponent_land_count),
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opponent_army_count=jnp.int32(opponent_army_count),
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timestep=jnp.int32(timestep),
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)
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@jax.jit
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def competition_step(state: GameState, actions: jnp.ndarray):
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state, actions = build_castles.apply_build_actions(state, actions)
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return deathtouch.step(state, actions, turn=800)
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class PolicyAgent:
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"""Direct policy agent that samples actions as in training/evaluation.
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On each turn it computes the masked policy logits over all legal moves and
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samples one action with `jrandom.categorical`, exactly mirroring
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`PolicyValueNetwork.__call__` used during rollout collection.
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"""
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def __init__(
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self,
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network: PolicyValueNetwork,
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seed: int = 0,
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):
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self.network = network
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self.key = jrandom.PRNGKey(seed)
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self._infer = eqx.filter_jit(
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lambda network, tensor, mask: network.policy_value(tensor, mask)
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)
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def warmup(self, height: int, width: int) -> None:
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"""Compile shape-dependent network and competition transition kernels."""
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grid = jnp.zeros((height, width), dtype=jnp.int32)
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grid = grid.at[0, 0].set(1).at[height - 1, width - 1].set(2)
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state = game.create_initial_state(grid)
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state = state._replace(
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armies=state.armies.at[0, 0].set(100).at[height - 1, width - 1].set(100),
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time=jnp.int32(100),
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)
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obs = game.get_observation(state, 0)
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self.policy_value(obs)
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actions = jnp.stack([jnp.asarray(PASS_ACTION), jnp.asarray(PASS_ACTION)])
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warmed_state, _ = competition_step(state, actions)
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jax.block_until_ready(warmed_state)
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def policy_value(self, obs: Observation):
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mask = competition_move_mask(obs)
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logits, value = self._infer(self.network, obs_to_tensor(obs), mask)
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return np.asarray(logits), float(value), mask
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def select(self, obs: Observation) -> tuple[np.ndarray, dict[str, float]]:
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"""Sample one action from the masked policy and report statistics."""
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started_at = time.perf_counter()
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logits, value, _ = self.policy_value(obs)
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self.key, sample_key = jrandom.split(self.key)
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index = int(jrandom.categorical(sample_key, jnp.asarray(logits)))
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height, width = obs.armies.shape
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action = _decode_policy_index(index, height, width)
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elapsed_ms = (time.perf_counter() - started_at) * 1000
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return action, {
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"value": value,
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"elapsed_ms": elapsed_ms,
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}
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# ---------------------------------------------------------------------------
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# Stdio driver
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# ---------------------------------------------------------------------------
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def _read_grid(stream, height: int):
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return [[int(value) for value in stream.readline().split()] for _ in range(height)]
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def parse_args():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("checkpoint", help="Equinox policy checkpoint")
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parser.add_argument("--seed", type=int, default=0)
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return parser.parse_args()
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def main():
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args = parse_args()
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handshake = sys.stdin.readline()
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if not handshake:
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return
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player_index, height, width = (int(value) for value in handshake.split())
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network = PolicyValueNetwork(jrandom.PRNGKey(args.seed))
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network = eqx.tree_deserialise_leaves(args.checkpoint, network)
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agent = PolicyAgent(network, seed=args.seed)
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agent.warmup(height, width)
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print(f"[competition] warmup complete for {height}x{width}", file=sys.stderr, flush=True)
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while True:
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scalar_line = sys.stdin.readline()
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if not scalar_line:
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return
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timestep, own_land, own_army, opponent_land, opponent_army = (
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int(value) for value in scalar_line.split()
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)
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type_grid = _read_grid(sys.stdin, height)
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owner_grid = _read_grid(sys.stdin, height)
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army_grid = _read_grid(sys.stdin, height)
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observation = observation_from_wire(
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timestep,
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own_land,
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own_army,
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opponent_land,
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opponent_army,
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type_grid,
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owner_grid,
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army_grid,
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)
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started_at = time.perf_counter()
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action, stats = agent.select(observation)
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elapsed_ms = (time.perf_counter() - started_at) * 1000
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print(
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f"[competition] turn={timestep} value={stats['value']:+.3f} "
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f"elapsed_ms={elapsed_ms:.1f}",
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file=sys.stderr,
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flush=True,
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)
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print(" ".join(str(int(value)) for value in action), flush=True)
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if __name__ == "__main__":
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main()
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equinox==0.13.8
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jaxtyping==0.3.11
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wadler-lindig==0.1.7
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@ -1,8 +0,0 @@
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#!/usr/bin/env bash
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set -euo pipefail
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# Competition entrypoint. The sandbox runs `run.sh` from the submission root;
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# cd to our own directory so relative paths work regardless of the cwd.
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# `python` is the sandbox's CPython 3.12 with jax/numpy pre-installed; equinox
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# and its deps are installed by build.sh from wheelhouse/.
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cd "$(dirname "$0")"
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exec python -u competition.py model.eqx
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@ -5,20 +5,12 @@ requires-python = ">=3.13"
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dependencies = [
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"equinox>=0.13.8",
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"generals-bots",
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"jax==0.11.0",
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||||
"jax[cuda]>=0.11.0",
|
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"jaxtyping>=0.3.11",
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"omegaconf>=2.3.1",
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"optax>=0.2.8",
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]
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[project.optional-dependencies]
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cuda = [
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"jax[cuda]==0.11.0"
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||||
]
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||||
cuda13 = [
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"jax[cuda13]==0.11.0"
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||||
]
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||||
|
||||
[tool.uv.sources]
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||||
generals-bots = { git = "https://github.com/strakam/generals-bots.git" }
|
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|
||||
|
|
|
|||
|
|
@ -1,11 +0,0 @@
|
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numpy==2.4.6
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scipy==1.18.0
|
||||
pandas==3.0.5
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||||
scikit-learn==1.9.0
|
||||
# On Linux, the default PyPI wheel bundles CUDA. For a CPU-only build, uncomment:
|
||||
jax==0.11.0
|
||||
numba==0.66.0
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||||
networkx==3.6.1
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||||
safetensors==0.8.0
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||||
gymnasium==1.3.0
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# torch==2.13.0
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||||
|
|
@ -1,3 +0,0 @@
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equinox==0.13.8
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jaxtyping==0.3.11
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||||
wadler-lindig==0.1.7
|
||||
|
|
@ -1,81 +0,0 @@
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|||
# This file was autogenerated by uv via the following command:
|
||||
# uv pip compile pyproject.toml -o requirements.txt
|
||||
absl-py==2.5.0
|
||||
# via optax
|
||||
antlr4-python3-runtime==4.9.3
|
||||
# via omegaconf
|
||||
bidict==0.23.1
|
||||
# via python-socketio
|
||||
certifi==2026.7.22
|
||||
# via requests
|
||||
charset-normalizer==3.4.9
|
||||
# via requests
|
||||
equinox==0.13.8
|
||||
# via general-bots-training (pyproject.toml)
|
||||
generals-bots @ git+https://github.com/strakam/generals-bots.git@9e3b9d13cca51caa1bb07db48bb85c9e90ce0462
|
||||
# via general-bots-training (pyproject.toml)
|
||||
h11==0.16.0
|
||||
# via wsproto
|
||||
idna==3.18
|
||||
# via requests
|
||||
jax==0.11.0
|
||||
# via
|
||||
# general-bots-training (pyproject.toml)
|
||||
# equinox
|
||||
# generals-bots
|
||||
# optax
|
||||
jaxlib==0.11.0
|
||||
# via
|
||||
# generals-bots
|
||||
# jax
|
||||
# optax
|
||||
jaxtyping==0.3.11
|
||||
# via
|
||||
# general-bots-training (pyproject.toml)
|
||||
# equinox
|
||||
ml-dtypes==0.5.4
|
||||
# via
|
||||
# jax
|
||||
# jaxlib
|
||||
numpy==2.5.1
|
||||
# via
|
||||
# generals-bots
|
||||
# jax
|
||||
# jaxlib
|
||||
# ml-dtypes
|
||||
# optax
|
||||
# scipy
|
||||
omegaconf==2.3.1
|
||||
# via general-bots-training (pyproject.toml)
|
||||
opt-einsum==3.4.0
|
||||
# via jax
|
||||
optax==0.2.8
|
||||
# via general-bots-training (pyproject.toml)
|
||||
pygame==2.6.1
|
||||
# via generals-bots
|
||||
python-engineio==4.13.4
|
||||
# via python-socketio
|
||||
python-socketio==5.16.4
|
||||
# via generals-bots
|
||||
pyyaml==6.0.3
|
||||
# via omegaconf
|
||||
requests==2.34.2
|
||||
# via python-socketio
|
||||
scipy==1.18.0
|
||||
# via
|
||||
# jax
|
||||
# jaxlib
|
||||
simple-websocket==1.1.0
|
||||
# via python-engineio
|
||||
typing-extensions==4.16.0
|
||||
# via equinox
|
||||
urllib3==2.7.0
|
||||
# via requests
|
||||
wadler-lindig==0.1.7
|
||||
# via
|
||||
# equinox
|
||||
# jaxtyping
|
||||
websocket-client==1.9.0
|
||||
# via python-socketio
|
||||
wsproto==1.3.2
|
||||
# via simple-websocket
|
||||
|
|
@ -1,342 +0,0 @@
|
|||
"""Competition stdio agent: direct policy sampling over a trained equinox checkpoint.
|
||||
|
||||
Self-contained competition bot. It speaks the wire protocol in
|
||||
`generals-bots/competition/protocol.py` (handshake, then one observation frame
|
||||
per turn, one action line per turn, EOF on stdin = game over) and, on each
|
||||
turn, samples an action from the policy network exactly as the model is used
|
||||
during training/evaluation — no tree search.
|
||||
|
||||
Unlike `scripts/mcts_agent.py`, this module does **not** import the
|
||||
`general_bots_training` training package. It inlines the policy-value network
|
||||
and the observation encoding so the script can be dropped into the competition
|
||||
sandbox with just the engine (`generals`), `equinox`, `jax`, and `numpy`
|
||||
available.
|
||||
|
||||
This is the search-free counterpart to `scripts/competition_puct.py`: it shares
|
||||
the same network and observation encoding, but selects an action by sampling
|
||||
from the masked policy (as in `PolicyValueNetwork.__call__`) instead of running
|
||||
particle PUCT.
|
||||
|
||||
Run it directly through the bundled matchup driver, e.g.:
|
||||
|
||||
PYTHONPATH=src:generals-bots .venv/bin/python \
|
||||
generals-bots/competition/matchup.py \
|
||||
scripts/competition.py --checkpoint ppo_model.eqx \
|
||||
generals-bots/competition/agents/expander_python/run.sh \
|
||||
--mode competition
|
||||
|
||||
or wrap it in a `run.sh` that passes the checkpoint path.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
|
||||
import equinox as eqx
|
||||
import jax
|
||||
import jax.numpy as jnp
|
||||
import jax.random as jrandom
|
||||
import numpy as np
|
||||
from generals.core import game
|
||||
from generals.core.action import compute_valid_move_mask
|
||||
from generals.core.game import GameState
|
||||
from generals.core.observation import Observation
|
||||
from generals.modifiers import build_castles, deathtouch
|
||||
|
||||
os.environ.setdefault("JAX_PLATFORMS", "cpu")
|
||||
|
||||
PASS_ACTION = np.array([1, 0, 0, 0, 0], dtype=np.int32)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Policy-value network (inlined from general_bots_training.network)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def obs_to_tensor(obs: Observation) -> jnp.ndarray:
|
||||
"""Encode an Observation into a (C, H, W) float32 tensor for the network.
|
||||
|
||||
Armies and army-counts are log-normalized; land counts are divided by the
|
||||
number of cells; scalar values are broadcast to spatial planes so a plain
|
||||
conv stack can consume them. Grid-size agnostic.
|
||||
"""
|
||||
H, W = obs.armies.shape
|
||||
armies = jnp.log1p(obs.armies.astype(jnp.float32)) / jnp.log(50.0)
|
||||
|
||||
def bcast(scalar):
|
||||
return jnp.broadcast_to(scalar.astype(jnp.float32), (H, W))
|
||||
|
||||
own_land = bcast(obs.owned_land_count / (H * W))
|
||||
own_army = bcast(jnp.log1p(obs.owned_army_count.astype(jnp.float32)) / jnp.log(50.0))
|
||||
opp_land = bcast(obs.opponent_land_count / (H * W))
|
||||
opp_army = bcast(jnp.log1p(obs.opponent_army_count.astype(jnp.float32)) / jnp.log(50.0))
|
||||
timestep = bcast(jnp.log1p(obs.timestep.astype(jnp.float32)) / jnp.log(1201.0))
|
||||
|
||||
return jnp.stack(
|
||||
[
|
||||
armies,
|
||||
obs.generals.astype(jnp.float32),
|
||||
obs.castles.astype(jnp.float32),
|
||||
obs.mountains.astype(jnp.float32),
|
||||
obs.neutral_cells.astype(jnp.float32),
|
||||
obs.owned_cells.astype(jnp.float32),
|
||||
obs.opponent_cells.astype(jnp.float32),
|
||||
obs.fog_cells.astype(jnp.float32),
|
||||
obs.structures_in_fog.astype(jnp.float32),
|
||||
own_land,
|
||||
own_army,
|
||||
opp_land,
|
||||
opp_army,
|
||||
timestep,
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
|
||||
|
||||
class PolicyValueNetwork(eqx.Module):
|
||||
"""Conv policy-value network.
|
||||
|
||||
Action layout: 4 full-move directions and 4 half-move (split) directions
|
||||
per source cell, plus one global pass action. Invalid moves are masked to
|
||||
-1e9; pass is always available.
|
||||
|
||||
The value head uses global average pooling over the spatial grid, so the
|
||||
network works for any board size without reshaping linear layers.
|
||||
"""
|
||||
|
||||
conv1: eqx.nn.Conv2d
|
||||
conv2: eqx.nn.Conv2d
|
||||
conv3: eqx.nn.Conv2d
|
||||
conv4: eqx.nn.Conv2d
|
||||
policy_conv: eqx.nn.Conv2d
|
||||
value_conv: eqx.nn.Conv2d
|
||||
value_linear1: eqx.nn.Linear
|
||||
value_linear2: eqx.nn.Linear
|
||||
|
||||
def __init__(self, key, in_channels: int = 14, channels=(32, 32, 32, 16)):
|
||||
keys = jrandom.split(key, 8)
|
||||
self.conv1 = eqx.nn.Conv2d(in_channels, channels[0], kernel_size=3, padding=1, key=keys[0])
|
||||
self.conv2 = eqx.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1, key=keys[1])
|
||||
self.conv3 = eqx.nn.Conv2d(channels[1], channels[2], kernel_size=3, padding=1, key=keys[2])
|
||||
self.conv4 = eqx.nn.Conv2d(channels[2], channels[3], kernel_size=3, padding=1, key=keys[3])
|
||||
# 9 = 4 dirs (full) + 4 dirs (half) + 1 pass
|
||||
self.policy_conv = eqx.nn.Conv2d(channels[3], 9, kernel_size=1, key=keys[4])
|
||||
self.value_conv = eqx.nn.Conv2d(channels[3], 4, kernel_size=1, key=keys[5])
|
||||
self.value_linear1 = eqx.nn.Linear(4, 64, key=keys[6])
|
||||
self.value_linear2 = eqx.nn.Linear(64, 1, key=keys[7])
|
||||
|
||||
def _features(self, obs):
|
||||
x = jax.nn.relu(self.conv1(obs))
|
||||
x = jax.nn.relu(self.conv2(x))
|
||||
x = jax.nn.relu(self.conv3(x))
|
||||
x = jax.nn.relu(self.conv4(x))
|
||||
return x
|
||||
|
||||
def _value_from_features(self, feat):
|
||||
v = jax.nn.relu(self.value_conv(feat)) # (4, H, W)
|
||||
v = v.mean(axis=(1, 2)) # (4,) global average pool
|
||||
v = jax.nn.relu(self.value_linear1(v))
|
||||
return self.value_linear2(v)[0]
|
||||
|
||||
def policy_value(self, obs, mask):
|
||||
"""Return deterministic masked policy logits and the critic value."""
|
||||
features = self._features(obs)
|
||||
return self._policy_logits(features, mask), self._value_from_features(features)
|
||||
|
||||
def _policy_logits(self, feat, mask):
|
||||
"""Return 8*H*W move logits plus one global pass logit."""
|
||||
logits = self.policy_conv(feat) # (9, H, W)
|
||||
mask_t = jnp.transpose(mask, (2, 0, 1)) # (4, H, W)
|
||||
penalty = (1.0 - mask_t) * -1e9
|
||||
move_penalty = jnp.concatenate([penalty, penalty], axis=0)
|
||||
move_logits = (logits[:8] + move_penalty).reshape(-1)
|
||||
pass_logit = jnp.mean(logits[8])[None]
|
||||
return jnp.concatenate([move_logits, pass_logit])
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Action selection (matches training/evaluation rollout)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@jax.jit
|
||||
def competition_move_mask(obs: Observation) -> jnp.ndarray:
|
||||
"""Legal move mask, identical to the one used during training/evaluation."""
|
||||
return compute_valid_move_mask(obs.armies, obs.owned_cells, obs.mountains)
|
||||
|
||||
|
||||
def _decode_policy_index(index: int, height: int, width: int) -> np.ndarray:
|
||||
cells = height * width
|
||||
if index == 8 * cells:
|
||||
return PASS_ACTION.copy()
|
||||
encoded_direction, position = divmod(index, cells)
|
||||
row, col = divmod(position, width)
|
||||
split = int(encoded_direction >= 4)
|
||||
direction = encoded_direction - 4 if split else encoded_direction
|
||||
return np.array([0, row, col, direction, split], dtype=np.int32)
|
||||
|
||||
|
||||
def observation_from_wire(
|
||||
timestep: int,
|
||||
owned_land_count: int,
|
||||
owned_army_count: int,
|
||||
opponent_land_count: int,
|
||||
opponent_army_count: int,
|
||||
type_grid,
|
||||
owner_grid,
|
||||
army_grid,
|
||||
) -> Observation:
|
||||
"""Convert a competition wire frame into the network's Observation type."""
|
||||
types = jnp.asarray(type_grid, dtype=jnp.int32)
|
||||
owners = jnp.asarray(owner_grid, dtype=jnp.int32)
|
||||
armies = jnp.asarray(army_grid, dtype=jnp.int32)
|
||||
visible = (types != 0) & (types != 5)
|
||||
return Observation(
|
||||
armies=armies,
|
||||
generals=types == 4,
|
||||
castles=types == 3,
|
||||
mountains=types == 2,
|
||||
neutral_cells=visible & (owners == 0) & (types != 2),
|
||||
owned_cells=owners == 1,
|
||||
opponent_cells=owners == 2,
|
||||
fog_cells=types == 0,
|
||||
structures_in_fog=types == 5,
|
||||
owned_land_count=jnp.int32(owned_land_count),
|
||||
owned_army_count=jnp.int32(owned_army_count),
|
||||
opponent_land_count=jnp.int32(opponent_land_count),
|
||||
opponent_army_count=jnp.int32(opponent_army_count),
|
||||
timestep=jnp.int32(timestep),
|
||||
)
|
||||
|
||||
|
||||
@jax.jit
|
||||
def competition_step(state: GameState, actions: jnp.ndarray):
|
||||
state, actions = build_castles.apply_build_actions(state, actions)
|
||||
return deathtouch.step(state, actions, turn=800)
|
||||
|
||||
|
||||
class PolicyAgent:
|
||||
"""Direct policy agent that samples actions as in training/evaluation.
|
||||
|
||||
On each turn it computes the masked policy logits over all legal moves and
|
||||
samples one action with `jrandom.categorical`, exactly mirroring
|
||||
`PolicyValueNetwork.__call__` used during rollout collection.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
network: PolicyValueNetwork,
|
||||
seed: int = 0,
|
||||
):
|
||||
self.network = network
|
||||
self.key = jrandom.PRNGKey(seed)
|
||||
self._infer = eqx.filter_jit(
|
||||
lambda network, tensor, mask: network.policy_value(tensor, mask)
|
||||
)
|
||||
|
||||
def warmup(self, height: int, width: int) -> None:
|
||||
"""Compile shape-dependent network and competition transition kernels."""
|
||||
grid = jnp.zeros((height, width), dtype=jnp.int32)
|
||||
grid = grid.at[0, 0].set(1).at[height - 1, width - 1].set(2)
|
||||
state = game.create_initial_state(grid)
|
||||
state = state._replace(
|
||||
armies=state.armies.at[0, 0].set(100).at[height - 1, width - 1].set(100),
|
||||
time=jnp.int32(100),
|
||||
)
|
||||
obs = game.get_observation(state, 0)
|
||||
self.policy_value(obs)
|
||||
actions = jnp.stack([jnp.asarray(PASS_ACTION), jnp.asarray(PASS_ACTION)])
|
||||
warmed_state, _ = competition_step(state, actions)
|
||||
jax.block_until_ready(warmed_state)
|
||||
|
||||
def policy_value(self, obs: Observation):
|
||||
mask = competition_move_mask(obs)
|
||||
logits, value = self._infer(self.network, obs_to_tensor(obs), mask)
|
||||
return np.asarray(logits), float(value), mask
|
||||
|
||||
def select(self, obs: Observation) -> tuple[np.ndarray, dict[str, float]]:
|
||||
"""Sample one action from the masked policy and report statistics."""
|
||||
started_at = time.perf_counter()
|
||||
logits, value, _ = self.policy_value(obs)
|
||||
self.key, sample_key = jrandom.split(self.key)
|
||||
index = int(jrandom.categorical(sample_key, jnp.asarray(logits)))
|
||||
height, width = obs.armies.shape
|
||||
action = _decode_policy_index(index, height, width)
|
||||
elapsed_ms = (time.perf_counter() - started_at) * 1000
|
||||
return action, {
|
||||
"value": value,
|
||||
"elapsed_ms": elapsed_ms,
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Stdio driver
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _read_grid(stream, height: int):
|
||||
return [[int(value) for value in stream.readline().split()] for _ in range(height)]
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("checkpoint", help="Equinox policy checkpoint")
|
||||
# Accepted for compatibility with the original run.sh; the direct policy
|
||||
# agent does no search, so these are ignored.
|
||||
parser.add_argument("--time-budget-ms", type=float, default=125.0)
|
||||
parser.add_argument("--max-simulations", type=int, default=128)
|
||||
parser.add_argument("--rollout-depth", type=int, default=2)
|
||||
parser.add_argument("--top-k", type=int, default=20)
|
||||
parser.add_argument("--seed", type=int, default=0)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
handshake = sys.stdin.readline()
|
||||
if not handshake:
|
||||
return
|
||||
player_index, height, width = (int(value) for value in handshake.split())
|
||||
|
||||
network = PolicyValueNetwork(jrandom.PRNGKey(args.seed))
|
||||
network = eqx.tree_deserialise_leaves(args.checkpoint, network)
|
||||
agent = PolicyAgent(network, seed=args.seed)
|
||||
agent.warmup(height, width)
|
||||
print(f"[competition] warmup complete for {height}x{width}", file=sys.stderr, flush=True)
|
||||
|
||||
while True:
|
||||
scalar_line = sys.stdin.readline()
|
||||
if not scalar_line:
|
||||
return
|
||||
timestep, own_land, own_army, opponent_land, opponent_army = (
|
||||
int(value) for value in scalar_line.split()
|
||||
)
|
||||
type_grid = _read_grid(sys.stdin, height)
|
||||
owner_grid = _read_grid(sys.stdin, height)
|
||||
army_grid = _read_grid(sys.stdin, height)
|
||||
observation = observation_from_wire(
|
||||
timestep,
|
||||
own_land,
|
||||
own_army,
|
||||
opponent_land,
|
||||
opponent_army,
|
||||
type_grid,
|
||||
owner_grid,
|
||||
army_grid,
|
||||
)
|
||||
|
||||
started_at = time.perf_counter()
|
||||
action, stats = agent.select(observation)
|
||||
elapsed_ms = (time.perf_counter() - started_at) * 1000
|
||||
print(
|
||||
f"[competition] turn={timestep} value={stats['value']:+.3f} "
|
||||
f"elapsed_ms={elapsed_ms:.1f}",
|
||||
file=sys.stderr,
|
||||
flush=True,
|
||||
)
|
||||
print(" ".join(str(int(value)) for value in action), flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,552 +0,0 @@
|
|||
"""Competition stdio agent: particle PUCT over a trained equinox checkpoint.
|
||||
|
||||
Self-contained competition bot. It speaks the wire protocol in
|
||||
`generals-bots/competition/protocol.py` (handshake, then one observation frame
|
||||
per turn, one action line per turn, EOF on stdin = game over) and runs
|
||||
deadline-bounded root PUCT using only the perspective-relative wire
|
||||
observation.
|
||||
|
||||
Unlike `scripts/mcts_agent.py`, this module does **not** import the
|
||||
`general_bots_training` training package. It inlines the policy-value network,
|
||||
the observation encoding, and the particle search so the script can be dropped
|
||||
into the competition sandbox with just the engine (`generals`), `equinox`,
|
||||
`jax`, and `numpy` available.
|
||||
|
||||
Run it directly through the bundled matchup driver, e.g.:
|
||||
|
||||
PYTHONPATH=src:generals-bots .venv/bin/python \
|
||||
generals-bots/competition/matchup.py \
|
||||
scripts/competition.py --checkpoint ppo_model.eqx \
|
||||
generals-bots/competition/agents/expander_python/run.sh \
|
||||
--mode competition
|
||||
|
||||
or wrap it in a `run.sh` that passes the checkpoint path.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from dataclasses import dataclass
|
||||
|
||||
import equinox as eqx
|
||||
import jax
|
||||
import jax.numpy as jnp
|
||||
import jax.random as jrandom
|
||||
import numpy as np
|
||||
from generals.core import game
|
||||
from generals.core.action import compute_valid_move_mask
|
||||
from generals.core.game import GameState
|
||||
from generals.core.observation import Observation
|
||||
from generals.modifiers import build_castles, deathtouch
|
||||
|
||||
os.environ.setdefault("JAX_PLATFORMS", "cpu")
|
||||
|
||||
PASS_ACTION = np.array([1, 0, 0, 0, 0], dtype=np.int32)
|
||||
|
||||
# Search/agent defaults, previously exposed as CLI flags. Kept as constants so
|
||||
# the script can be launched by a bare `run.sh` with no extra arguments.
|
||||
TIME_BUDGET_MS = 125.0
|
||||
MAX_SIMULATIONS = 128
|
||||
ROLLOUT_DEPTH = 2
|
||||
TOP_K = 20
|
||||
SEED = 0
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Policy-value network (inlined from general_bots_training.network)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def obs_to_tensor(obs: Observation) -> jnp.ndarray:
|
||||
"""Encode an Observation into a (C, H, W) float32 tensor for the network.
|
||||
|
||||
Armies and army-counts are log-normalized; land counts are divided by the
|
||||
number of cells; scalar values are broadcast to spatial planes so a plain
|
||||
conv stack can consume them. Grid-size agnostic.
|
||||
"""
|
||||
H, W = obs.armies.shape
|
||||
armies = jnp.log1p(obs.armies.astype(jnp.float32)) / jnp.log(50.0)
|
||||
|
||||
def bcast(scalar):
|
||||
return jnp.broadcast_to(scalar.astype(jnp.float32), (H, W))
|
||||
|
||||
own_land = bcast(obs.owned_land_count / (H * W))
|
||||
own_army = bcast(jnp.log1p(obs.owned_army_count.astype(jnp.float32)) / jnp.log(50.0))
|
||||
opp_land = bcast(obs.opponent_land_count / (H * W))
|
||||
opp_army = bcast(jnp.log1p(obs.opponent_army_count.astype(jnp.float32)) / jnp.log(50.0))
|
||||
timestep = bcast(jnp.log1p(obs.timestep.astype(jnp.float32)) / jnp.log(1201.0))
|
||||
|
||||
return jnp.stack(
|
||||
[
|
||||
armies,
|
||||
obs.generals.astype(jnp.float32),
|
||||
obs.castles.astype(jnp.float32),
|
||||
obs.mountains.astype(jnp.float32),
|
||||
obs.neutral_cells.astype(jnp.float32),
|
||||
obs.owned_cells.astype(jnp.float32),
|
||||
obs.opponent_cells.astype(jnp.float32),
|
||||
obs.fog_cells.astype(jnp.float32),
|
||||
obs.structures_in_fog.astype(jnp.float32),
|
||||
own_land,
|
||||
own_army,
|
||||
opp_land,
|
||||
opp_army,
|
||||
timestep,
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
|
||||
|
||||
class PolicyValueNetwork(eqx.Module):
|
||||
"""Conv policy-value network.
|
||||
|
||||
Action layout: 4 full-move directions and 4 half-move (split) directions
|
||||
per source cell, plus one global pass action. Invalid moves are masked to
|
||||
-1e9; pass is always available.
|
||||
|
||||
The value head uses global average pooling over the spatial grid, so the
|
||||
network works for any board size without reshaping linear layers.
|
||||
"""
|
||||
|
||||
conv1: eqx.nn.Conv2d
|
||||
conv2: eqx.nn.Conv2d
|
||||
conv3: eqx.nn.Conv2d
|
||||
conv4: eqx.nn.Conv2d
|
||||
policy_conv: eqx.nn.Conv2d
|
||||
value_conv: eqx.nn.Conv2d
|
||||
value_linear1: eqx.nn.Linear
|
||||
value_linear2: eqx.nn.Linear
|
||||
|
||||
def __init__(self, key, in_channels: int = 14, channels=(32, 32, 32, 16)):
|
||||
keys = jrandom.split(key, 8)
|
||||
self.conv1 = eqx.nn.Conv2d(in_channels, channels[0], kernel_size=3, padding=1, key=keys[0])
|
||||
self.conv2 = eqx.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1, key=keys[1])
|
||||
self.conv3 = eqx.nn.Conv2d(channels[1], channels[2], kernel_size=3, padding=1, key=keys[2])
|
||||
self.conv4 = eqx.nn.Conv2d(channels[2], channels[3], kernel_size=3, padding=1, key=keys[3])
|
||||
# 9 = 4 dirs (full) + 4 dirs (half) + 1 pass
|
||||
self.policy_conv = eqx.nn.Conv2d(channels[3], 9, kernel_size=1, key=keys[4])
|
||||
self.value_conv = eqx.nn.Conv2d(channels[3], 4, kernel_size=1, key=keys[5])
|
||||
self.value_linear1 = eqx.nn.Linear(4, 64, key=keys[6])
|
||||
self.value_linear2 = eqx.nn.Linear(64, 1, key=keys[7])
|
||||
|
||||
def _features(self, obs):
|
||||
x = jax.nn.relu(self.conv1(obs))
|
||||
x = jax.nn.relu(self.conv2(x))
|
||||
x = jax.nn.relu(self.conv3(x))
|
||||
x = jax.nn.relu(self.conv4(x))
|
||||
return x
|
||||
|
||||
def _value_from_features(self, feat):
|
||||
v = jax.nn.relu(self.value_conv(feat)) # (4, H, W)
|
||||
v = v.mean(axis=(1, 2)) # (4,) global average pool
|
||||
v = jax.nn.relu(self.value_linear1(v))
|
||||
return self.value_linear2(v)[0]
|
||||
|
||||
def policy_value(self, obs, mask):
|
||||
"""Return deterministic masked policy logits and the critic value."""
|
||||
features = self._features(obs)
|
||||
return self._policy_logits(features, mask), self._value_from_features(features)
|
||||
|
||||
def _policy_logits(self, feat, mask):
|
||||
"""Return 8*H*W move logits plus one global pass logit."""
|
||||
logits = self.policy_conv(feat) # (9, H, W)
|
||||
mask_t = jnp.transpose(mask, (2, 0, 1)) # (4, H, W)
|
||||
penalty = (1.0 - mask_t) * -1e9
|
||||
move_penalty = jnp.concatenate([penalty, penalty], axis=0)
|
||||
move_logits = (logits[:8] + move_penalty).reshape(-1)
|
||||
pass_logit = jnp.mean(logits[8])[None]
|
||||
return jnp.concatenate([move_logits, pass_logit])
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Particle PUCT search (inlined from general_bots_training.mcts)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class MCTSConfig:
|
||||
time_budget_ms: float = 100.0
|
||||
max_simulations: int = 128
|
||||
rollout_depth: int = 2
|
||||
top_k: int = 20
|
||||
opponent_top_k: int = 12
|
||||
max_build_actions: int = 3
|
||||
c_puct: float = 1.5
|
||||
value_scale: float = 5.0
|
||||
|
||||
|
||||
@jax.jit
|
||||
def competition_move_mask(obs: Observation) -> jnp.ndarray:
|
||||
"""Legal move mask that conservatively blocks unresolved fog structures."""
|
||||
blocked = obs.mountains | obs.structures_in_fog
|
||||
return compute_valid_move_mask(obs.armies, obs.owned_cells, blocked)
|
||||
|
||||
|
||||
@jax.jit
|
||||
def build_cost_grid(obs: Observation) -> jnp.ndarray:
|
||||
"""Exact own-castle build costs derivable from a fog observation."""
|
||||
structures = ((obs.castles | obs.generals) & obs.owned_cells).astype(jnp.int32)
|
||||
height, width = structures.shape
|
||||
radius = 6
|
||||
padded = jnp.pad(structures, radius)
|
||||
costs = jnp.full((height, width), 35, dtype=jnp.int32)
|
||||
for row_offset in range(-radius, radius + 1):
|
||||
for col_offset in range(-radius, radius + 1):
|
||||
surcharge = 14 - 2 * (abs(row_offset) + abs(col_offset))
|
||||
if surcharge > 0:
|
||||
shifted = padded[
|
||||
radius + row_offset : radius + row_offset + height,
|
||||
radius + col_offset : radius + col_offset + width,
|
||||
]
|
||||
costs = costs + surcharge * shifted
|
||||
return costs
|
||||
|
||||
|
||||
@jax.jit
|
||||
def valid_build_mask(obs: Observation) -> jnp.ndarray:
|
||||
costs = build_cost_grid(obs)
|
||||
plain_owned = obs.owned_cells & ~obs.generals & ~obs.castles
|
||||
return plain_owned & (obs.armies >= costs)
|
||||
|
||||
|
||||
def _decode_policy_index(index: int, height: int, width: int) -> np.ndarray:
|
||||
cells = height * width
|
||||
if index == 8 * cells:
|
||||
return PASS_ACTION.copy()
|
||||
encoded_direction, position = divmod(index, cells)
|
||||
row, col = divmod(position, width)
|
||||
split = int(encoded_direction >= 4)
|
||||
direction = encoded_direction - 4 if split else encoded_direction
|
||||
return np.array([0, row, col, direction, split], dtype=np.int32)
|
||||
|
||||
|
||||
def observation_from_wire(
|
||||
timestep: int,
|
||||
owned_land_count: int,
|
||||
owned_army_count: int,
|
||||
opponent_land_count: int,
|
||||
opponent_army_count: int,
|
||||
type_grid,
|
||||
owner_grid,
|
||||
army_grid,
|
||||
) -> Observation:
|
||||
"""Convert a competition wire frame into the network's Observation type."""
|
||||
types = jnp.asarray(type_grid, dtype=jnp.int32)
|
||||
owners = jnp.asarray(owner_grid, dtype=jnp.int32)
|
||||
armies = jnp.asarray(army_grid, dtype=jnp.int32)
|
||||
visible = (types != 0) & (types != 5)
|
||||
return Observation(
|
||||
armies=armies,
|
||||
generals=types == 4,
|
||||
castles=types == 3,
|
||||
mountains=types == 2,
|
||||
neutral_cells=visible & (owners == 0) & (types != 2),
|
||||
owned_cells=owners == 1,
|
||||
opponent_cells=owners == 2,
|
||||
fog_cells=types == 0,
|
||||
structures_in_fog=types == 5,
|
||||
owned_land_count=jnp.int32(owned_land_count),
|
||||
owned_army_count=jnp.int32(owned_army_count),
|
||||
opponent_land_count=jnp.int32(opponent_land_count),
|
||||
opponent_army_count=jnp.int32(opponent_army_count),
|
||||
timestep=jnp.int32(timestep),
|
||||
)
|
||||
|
||||
|
||||
def sample_determinization(
|
||||
obs: Observation, player_index: int, rng: np.random.Generator
|
||||
) -> GameState:
|
||||
"""Sample a conservative hidden state consistent with visible cells/totals."""
|
||||
armies = np.asarray(obs.armies, dtype=np.int32).copy()
|
||||
owned = np.asarray(obs.owned_cells, dtype=bool)
|
||||
visible_opponent = np.asarray(obs.opponent_cells, dtype=bool)
|
||||
mountains = np.asarray(obs.mountains | obs.structures_in_fog, dtype=bool)
|
||||
castles = np.asarray(obs.castles, dtype=bool)
|
||||
generals = np.asarray(obs.generals, dtype=bool).copy()
|
||||
fog = np.asarray(obs.fog_cells, dtype=bool) & ~mountains
|
||||
|
||||
opponent = visible_opponent.copy()
|
||||
hidden_candidates = np.argwhere(fog & ~owned)
|
||||
missing_land = max(0, int(obs.opponent_land_count) - int(opponent.sum()))
|
||||
if len(hidden_candidates):
|
||||
chosen = hidden_candidates[
|
||||
rng.choice(
|
||||
len(hidden_candidates),
|
||||
size=min(missing_land, len(hidden_candidates)),
|
||||
replace=False,
|
||||
)
|
||||
]
|
||||
opponent[chosen[:, 0], chosen[:, 1]] = True
|
||||
|
||||
visible_enemy_general = generals & opponent
|
||||
if not visible_enemy_general.any():
|
||||
candidates = np.argwhere((opponent | fog) & ~owned & ~mountains)
|
||||
if len(candidates):
|
||||
row, col = candidates[rng.integers(len(candidates))]
|
||||
generals[row, col] = True
|
||||
opponent[row, col] = True
|
||||
|
||||
hidden_opponent = opponent & ~visible_opponent
|
||||
remaining_army = max(0, int(obs.opponent_army_count) - int(armies[visible_opponent].sum()))
|
||||
hidden_cells = np.argwhere(hidden_opponent)
|
||||
if len(hidden_cells):
|
||||
base = min(remaining_army, len(hidden_cells))
|
||||
armies[hidden_opponent] = 0
|
||||
armies[hidden_cells[:base, 0], hidden_cells[:base, 1]] = 1
|
||||
remaining_army -= base
|
||||
if remaining_army:
|
||||
allocations = rng.multinomial(
|
||||
remaining_army, np.full(len(hidden_cells), 1 / len(hidden_cells))
|
||||
)
|
||||
armies[hidden_cells[:, 0], hidden_cells[:, 1]] += allocations.astype(np.int32)
|
||||
|
||||
ownership_relative = np.stack([owned, opponent])
|
||||
if player_index == 1:
|
||||
ownership = ownership_relative[::-1]
|
||||
else:
|
||||
ownership = ownership_relative
|
||||
passable = ~mountains
|
||||
neutral = passable & ~ownership[0] & ~ownership[1]
|
||||
|
||||
general_positions = []
|
||||
for absolute_player in range(2):
|
||||
positions = np.argwhere(generals & ownership[absolute_player])
|
||||
if len(positions):
|
||||
general_positions.append(positions[0])
|
||||
else:
|
||||
fallback = np.argwhere(ownership[absolute_player] & passable)
|
||||
general_positions.append(fallback[0] if len(fallback) else np.array([0, 0]))
|
||||
|
||||
return GameState(
|
||||
armies=jnp.asarray(armies),
|
||||
ownership=jnp.asarray(ownership),
|
||||
ownership_neutral=jnp.asarray(neutral),
|
||||
generals=jnp.asarray(generals),
|
||||
castles=jnp.asarray(castles),
|
||||
mountains=jnp.asarray(mountains),
|
||||
passable=jnp.asarray(passable),
|
||||
general_positions=jnp.asarray(general_positions, dtype=jnp.int32),
|
||||
time=jnp.asarray(obs.timestep, dtype=jnp.int32),
|
||||
winner=jnp.int32(-1),
|
||||
pool_idx=jnp.int32(0),
|
||||
)
|
||||
|
||||
|
||||
@jax.jit
|
||||
def competition_step(state: GameState, actions: jnp.ndarray):
|
||||
state, actions = build_castles.apply_build_actions(state, actions)
|
||||
return deathtouch.step(state, actions, turn=800)
|
||||
|
||||
|
||||
class ParticlePUCT:
|
||||
"""Deadline-bounded root PUCT with policy-guided simultaneous rollouts."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
network: PolicyValueNetwork,
|
||||
player_index: int,
|
||||
config: MCTSConfig = MCTSConfig(),
|
||||
seed: int = 0,
|
||||
):
|
||||
self.network = network
|
||||
self.player_index = player_index
|
||||
self.config = config
|
||||
self.rng = np.random.default_rng(seed)
|
||||
self._infer = eqx.filter_jit(
|
||||
lambda network, tensor, mask: network.policy_value(tensor, mask)
|
||||
)
|
||||
|
||||
def warmup(self, height: int, width: int) -> None:
|
||||
"""Compile shape-dependent network and competition transition kernels."""
|
||||
grid = jnp.zeros((height, width), dtype=jnp.int32)
|
||||
grid = grid.at[0, 0].set(1).at[height - 1, width - 1].set(2)
|
||||
state = game.create_initial_state(grid)
|
||||
state = state._replace(
|
||||
armies=state.armies.at[0, 0].set(100).at[height - 1, width - 1].set(100),
|
||||
time=jnp.int32(100),
|
||||
)
|
||||
obs = game.get_observation(state, self.player_index)
|
||||
self.policy_value(obs)
|
||||
actions = jnp.stack([jnp.asarray(PASS_ACTION), jnp.asarray(PASS_ACTION)])
|
||||
warmed_state, _ = competition_step(state, actions)
|
||||
jax.block_until_ready(warmed_state)
|
||||
candidates, _ = self.candidates(obs)
|
||||
self._simulate(obs, candidates[0])
|
||||
self._simulate(obs, candidates[min(1, len(candidates) - 1)])
|
||||
|
||||
def policy_value(self, obs: Observation):
|
||||
mask = competition_move_mask(obs)
|
||||
logits, value = self._infer(self.network, obs_to_tensor(obs), mask)
|
||||
return np.asarray(logits), float(value), mask
|
||||
|
||||
def candidates(self, obs: Observation, top_k: int | None = None):
|
||||
logits, _, _ = self.policy_value(obs)
|
||||
height, width = obs.armies.shape
|
||||
valid_indices = np.flatnonzero(logits > -1e8)
|
||||
count = min(top_k or self.config.top_k, len(valid_indices))
|
||||
ranked = np.argsort(logits[valid_indices])[::-1][:count]
|
||||
indices = valid_indices[ranked]
|
||||
selected_logits = logits[indices]
|
||||
selected_logits = selected_logits - selected_logits.max()
|
||||
priors = np.exp(selected_logits)
|
||||
actions = [_decode_policy_index(int(index), height, width) for index in indices]
|
||||
|
||||
build_mask = np.asarray(valid_build_mask(obs))
|
||||
build_positions = np.argwhere(build_mask)
|
||||
if len(build_positions):
|
||||
costs = np.asarray(build_cost_grid(obs))
|
||||
armies = np.asarray(obs.armies)
|
||||
scores = np.array([armies[r, c] - costs[r, c] for r, c in build_positions])
|
||||
order = np.argsort(scores)[::-1][: self.config.max_build_actions]
|
||||
build_prior = max(float(priors.sum()) * 0.05, 1e-3)
|
||||
for position_index in order:
|
||||
row, col = build_positions[position_index]
|
||||
actions.append(np.array([2, row, col, 0, 0], dtype=np.int32))
|
||||
priors = np.append(priors, build_prior)
|
||||
|
||||
priors = priors / priors.sum()
|
||||
return actions, priors
|
||||
|
||||
def _sample_policy_action(self, obs: Observation, top_k: int) -> np.ndarray:
|
||||
actions, priors = self.candidates(obs, top_k)
|
||||
return actions[int(self.rng.choice(len(actions), p=priors))]
|
||||
|
||||
def _leaf_value(self, state: GameState, info) -> float:
|
||||
if bool(info.is_done):
|
||||
winner = int(info.winner)
|
||||
return 0.0 if winner < 0 else (1.0 if winner == self.player_index else -1.0)
|
||||
obs = game.get_observation(state, self.player_index)
|
||||
_, value, _ = self.policy_value(obs)
|
||||
return float(np.tanh(value / self.config.value_scale))
|
||||
|
||||
def _simulate(self, root_obs: Observation, root_action: np.ndarray) -> float:
|
||||
state = sample_determinization(root_obs, self.player_index, self.rng)
|
||||
info = game.get_info(state)
|
||||
our_action = root_action
|
||||
for _ in range(self.config.rollout_depth):
|
||||
opponent_index = 1 - self.player_index
|
||||
opponent_obs = game.get_observation(state, opponent_index)
|
||||
opponent_action = self._sample_policy_action(opponent_obs, self.config.opponent_top_k)
|
||||
joint_actions = [None, None]
|
||||
joint_actions[self.player_index] = our_action
|
||||
joint_actions[opponent_index] = opponent_action
|
||||
state, info = competition_step(state, jnp.asarray(joint_actions, dtype=jnp.int32))
|
||||
jax.block_until_ready(state)
|
||||
if bool(info.is_done):
|
||||
break
|
||||
our_obs = game.get_observation(state, self.player_index)
|
||||
our_action = self._sample_policy_action(our_obs, self.config.top_k)
|
||||
return self._leaf_value(state, info)
|
||||
|
||||
def search(self, obs: Observation) -> tuple[np.ndarray, dict[str, float]]:
|
||||
started_at = time.perf_counter()
|
||||
deadline = started_at + self.config.time_budget_ms / 1000.0
|
||||
actions, priors = self.candidates(obs)
|
||||
visits = np.zeros(len(actions), dtype=np.int32)
|
||||
value_sums = np.zeros(len(actions), dtype=np.float64)
|
||||
simulations = 0
|
||||
estimated_simulation_seconds = 0.0
|
||||
|
||||
while (
|
||||
simulations < self.config.max_simulations
|
||||
and time.perf_counter() + estimated_simulation_seconds < deadline
|
||||
):
|
||||
total_visits = max(1, int(visits.sum()))
|
||||
q_values = np.divide(
|
||||
value_sums,
|
||||
visits,
|
||||
out=np.zeros_like(value_sums),
|
||||
where=visits > 0,
|
||||
)
|
||||
scores = q_values + self.config.c_puct * priors * np.sqrt(total_visits) / (1 + visits)
|
||||
action_index = int(np.argmax(scores))
|
||||
simulation_started_at = time.perf_counter()
|
||||
value = self._simulate(obs, actions[action_index])
|
||||
simulation_elapsed = time.perf_counter() - simulation_started_at
|
||||
estimated_simulation_seconds = max(estimated_simulation_seconds, simulation_elapsed)
|
||||
visits[action_index] += 1
|
||||
value_sums[action_index] += value
|
||||
simulations += 1
|
||||
|
||||
selected = int(np.argmax(visits)) if simulations else int(np.argmax(priors))
|
||||
return actions[selected], {
|
||||
"simulations": float(simulations),
|
||||
"selected_visits": float(visits[selected]),
|
||||
"elapsed_budget_ms": self.config.time_budget_ms,
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Stdio driver
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _read_grid(stream, height: int):
|
||||
return [[int(value) for value in stream.readline().split()] for _ in range(height)]
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("checkpoint", help="Equinox policy checkpoint")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
handshake = sys.stdin.readline()
|
||||
if not handshake:
|
||||
return
|
||||
player_index, height, width = (int(value) for value in handshake.split())
|
||||
|
||||
network = PolicyValueNetwork(jrandom.PRNGKey(SEED))
|
||||
network = eqx.tree_deserialise_leaves(args.checkpoint, network)
|
||||
search = ParticlePUCT(
|
||||
network,
|
||||
player_index,
|
||||
MCTSConfig(
|
||||
time_budget_ms=TIME_BUDGET_MS,
|
||||
max_simulations=MAX_SIMULATIONS,
|
||||
rollout_depth=ROLLOUT_DEPTH,
|
||||
top_k=TOP_K,
|
||||
),
|
||||
seed=SEED,
|
||||
)
|
||||
search.warmup(height, width)
|
||||
print(f"[competition] warmup complete for {height}x{width}", file=sys.stderr, flush=True)
|
||||
|
||||
while True:
|
||||
scalar_line = sys.stdin.readline()
|
||||
if not scalar_line:
|
||||
return
|
||||
timestep, own_land, own_army, opponent_land, opponent_army = (
|
||||
int(value) for value in scalar_line.split()
|
||||
)
|
||||
type_grid = _read_grid(sys.stdin, height)
|
||||
owner_grid = _read_grid(sys.stdin, height)
|
||||
army_grid = _read_grid(sys.stdin, height)
|
||||
observation = observation_from_wire(
|
||||
timestep,
|
||||
own_land,
|
||||
own_army,
|
||||
opponent_land,
|
||||
opponent_army,
|
||||
type_grid,
|
||||
owner_grid,
|
||||
army_grid,
|
||||
)
|
||||
|
||||
started_at = time.perf_counter()
|
||||
action, stats = search.search(observation)
|
||||
elapsed_ms = (time.perf_counter() - started_at) * 1000
|
||||
print(
|
||||
f"[competition] turn={timestep} simulations={int(stats['simulations'])} "
|
||||
f"elapsed_ms={elapsed_ms:.1f}",
|
||||
file=sys.stderr,
|
||||
flush=True,
|
||||
)
|
||||
print(" ".join(str(int(value)) for value in action), flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,123 +0,0 @@
|
|||
"""Evaluate Generals.io agents by playing them against each other.
|
||||
|
||||
Run with `.venv/bin/python scripts/evaluate.py` or `uv run python scripts/evaluate.py`.
|
||||
Override defaults with OmegaConf arguments such as `num_games=200 agent0=hunter`.
|
||||
|
||||
Each agent is either one of the bundled JAX agents (`random`, `expander`,
|
||||
`hunter`) or a trained policy loaded from an equinox checkpoint via
|
||||
`agent0=model:ppo_model.eqx`. Games are run in a single vmapped batch of
|
||||
`num_games` parallel envs.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass
|
||||
|
||||
import jax
|
||||
import jax.numpy as jnp
|
||||
import jax.random as jrandom
|
||||
from generals.core import game
|
||||
from generals.core.env import GeneralsEnv
|
||||
from omegaconf import DictConfig, OmegaConf
|
||||
|
||||
from general_bots_training.opponents import make_opponent
|
||||
|
||||
|
||||
@dataclass
|
||||
class EvalConfig:
|
||||
grid_dims: tuple[int, int] = (21, 21)
|
||||
truncation: int = 1200
|
||||
num_games: int = 100
|
||||
agent0: str = "random"
|
||||
agent1: str = "expander"
|
||||
seed: int = 0
|
||||
|
||||
|
||||
def load_config(args: list[str] | None = None) -> DictConfig:
|
||||
"""Merge CLI overrides into the typed default evaluation configuration."""
|
||||
defaults = OmegaConf.structured(EvalConfig)
|
||||
return OmegaConf.merge(defaults, OmegaConf.from_cli(args)) # type: ignore
|
||||
|
||||
|
||||
def main(config: DictConfig):
|
||||
key = jrandom.PRNGKey(config.seed)
|
||||
key, env_key = jrandom.split(key, 2)
|
||||
|
||||
agent0 = make_opponent(config.agent0)
|
||||
agent1 = make_opponent(config.agent1)
|
||||
label0, label1 = config.agent0, config.agent1
|
||||
|
||||
env = GeneralsEnv(grid_dims=tuple(config.grid_dims), truncation=config.truncation)
|
||||
pool, _ = env.reset(env_key)
|
||||
get_obs = game.get_full_observation if env.perfect_info else game.get_observation
|
||||
|
||||
n = config.num_games
|
||||
key, init_key = jrandom.split(key)
|
||||
init_keys = jrandom.split(init_key, n)
|
||||
states = jax.vmap(env.init_state)(init_keys)
|
||||
states = states._replace(pool_idx=jnp.arange(n, dtype=states.pool_idx.dtype) % env.pool_size)
|
||||
|
||||
step_env = jax.vmap(env.step, in_axes=(0, 0, None))
|
||||
|
||||
def run_episode(states, key):
|
||||
"""Play one full game per env; return final winner per env.
|
||||
|
||||
The env auto-resets on done and overwrites `state.winner` with -1, so we
|
||||
capture the winner from `timestep.info` on the first done step per env and
|
||||
latch it for the remainder of the scan.
|
||||
"""
|
||||
|
||||
def body(carry, _):
|
||||
states, latched_winner, key = carry
|
||||
key, p0_key, p1_key = jrandom.split(key, 3)
|
||||
obs_p0 = jax.vmap(lambda s: get_obs(s, 0))(states)
|
||||
obs_p1 = jax.vmap(lambda s: get_obs(s, 1))(states)
|
||||
keys_p0 = jrandom.split(p0_key, n)
|
||||
keys_p1 = jrandom.split(p1_key, n)
|
||||
actions_p0 = jax.vmap(agent0.act)(obs_p0, keys_p0)
|
||||
actions_p1 = jax.vmap(agent1.act)(obs_p1, keys_p1)
|
||||
actions = jnp.stack([actions_p0, actions_p1], axis=1)
|
||||
timesteps, states = step_env(states, actions, pool)
|
||||
done = timesteps.terminated | timesteps.truncated
|
||||
# On the first done, latch the winner (truncated games stay at -1 = draw).
|
||||
new_winner = jnp.where(
|
||||
done & (latched_winner < 0), timesteps.info.winner, latched_winner
|
||||
)
|
||||
return (states, new_winner, key), done
|
||||
|
||||
# Run a fixed number of steps equal to the truncation length, which
|
||||
# guarantees every env has terminated or truncated at least once.
|
||||
(states, latched_winner, key), _ = jax.lax.scan(
|
||||
body, (states, jnp.full(n, -1, dtype=jnp.int32), key), None, length=config.truncation
|
||||
)
|
||||
return latched_winner
|
||||
|
||||
print("Generals.io evaluation")
|
||||
print(OmegaConf.to_yaml(config, resolve=True).rstrip())
|
||||
print(f"device: {jax.devices()[0]}")
|
||||
print(f"agent0: {label0}")
|
||||
print(f"agent1: {label1}")
|
||||
print(f"games: {n}")
|
||||
print()
|
||||
print("warming up (jit compile)...")
|
||||
winners = run_episode(states, key)
|
||||
jax.block_until_ready(winners)
|
||||
print("warming up done\n")
|
||||
|
||||
print("playing...")
|
||||
winners = run_episode(states, key)
|
||||
jax.block_until_ready(winners)
|
||||
|
||||
wins0 = int(jnp.sum(winners == 0))
|
||||
wins1 = int(jnp.sum(winners == 1))
|
||||
draws = int(jnp.sum(winners < 0))
|
||||
|
||||
print("\nsummary")
|
||||
print("-" * 40)
|
||||
print(f"agent0 ({label0}): {wins0:4d} wins ({wins0 / n * 100:.1f}%)")
|
||||
print(f"agent1 ({label1}): {wins1:4d} wins ({wins1 / n * 100:.1f}%)")
|
||||
print(f"draws: {draws:4d} ({draws / n * 100:.1f}%)")
|
||||
print("-" * 40)
|
||||
print(f"total games: {n}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main(load_config())
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
cd "$(dirname "$0")"
|
||||
exec python -u competition.py ../ppo_model.eqx
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
from .mcts import MCTSConfig, ParticlePUCT
|
||||
from .network import PolicyValueNetwork, obs_to_tensor
|
||||
from .opponents import ModelOpponent, Opponent, SelfPlayOpponent, StaticOpponent, make_opponent
|
||||
from .opponents import Opponent, SelfPlayOpponent, StaticOpponent, make_opponent
|
||||
from .ppo import compute_advantages_and_returns, compute_gae, make_train_epoch, ppo_loss
|
||||
from .rollout import make_collect_rollout, make_rollout_step
|
||||
|
||||
|
|
@ -17,7 +17,6 @@ __all__ = [
|
|||
"make_rollout_step",
|
||||
"make_opponent",
|
||||
"StaticOpponent",
|
||||
"ModelOpponent",
|
||||
"SelfPlayOpponent",
|
||||
"Opponent",
|
||||
]
|
||||
|
|
|
|||
BIN
src/general_bots_training/__pycache__/__init__.cpython-313.pyc
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BIN
src/general_bots_training/__pycache__/__init__.cpython-313.pyc
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Binary file not shown.
BIN
src/general_bots_training/__pycache__/mcts.cpython-313.pyc
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BIN
src/general_bots_training/__pycache__/mcts.cpython-313.pyc
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Binary file not shown.
BIN
src/general_bots_training/__pycache__/network.cpython-313.pyc
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BIN
src/general_bots_training/__pycache__/network.cpython-313.pyc
Normal file
Binary file not shown.
BIN
src/general_bots_training/__pycache__/opponents.cpython-313.pyc
Normal file
BIN
src/general_bots_training/__pycache__/opponents.cpython-313.pyc
Normal file
Binary file not shown.
BIN
src/general_bots_training/__pycache__/ppo.cpython-313.pyc
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BIN
src/general_bots_training/__pycache__/ppo.cpython-313.pyc
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Binary file not shown.
BIN
src/general_bots_training/__pycache__/rollout.cpython-313.pyc
Normal file
BIN
src/general_bots_training/__pycache__/rollout.cpython-313.pyc
Normal file
Binary file not shown.
BIN
src/general_bots_training/__pycache__/train.cpython-313.pyc
Normal file
BIN
src/general_bots_training/__pycache__/train.cpython-313.pyc
Normal file
Binary file not shown.
|
|
@ -4,15 +4,10 @@ from typing import Protocol
|
|||
|
||||
from dataclasses import dataclass
|
||||
|
||||
import equinox as eqx
|
||||
import jax.numpy as jnp
|
||||
import jax.random as jrandom
|
||||
from generals.agents import Agent, ExpanderAgent, HunterAgent, RandomAgent
|
||||
from generals.core.action import compute_valid_move_mask
|
||||
from generals.core.observation import Observation
|
||||
|
||||
from .network import PolicyValueNetwork, obs_to_tensor
|
||||
|
||||
|
||||
class StaticOpponent(Protocol):
|
||||
"""Opponent policy that carries no per-environment mutable state."""
|
||||
|
|
@ -25,21 +20,6 @@ class SelfPlayOpponent:
|
|||
"""Marker selecting the current training network as player 1."""
|
||||
|
||||
|
||||
class ModelOpponent:
|
||||
"""Static opponent that plays a fixed policy loaded from an equinox checkpoint."""
|
||||
|
||||
def __init__(self, network: PolicyValueNetwork):
|
||||
self._network = network
|
||||
|
||||
def act(self, observation: Observation, key: jnp.ndarray) -> jnp.ndarray:
|
||||
obs_arr = obs_to_tensor(observation)
|
||||
mask = compute_valid_move_mask(
|
||||
observation.armies, observation.owned_cells, observation.mountains
|
||||
)
|
||||
action, _, _, _ = self._network(obs_arr, mask, key, None)
|
||||
return action
|
||||
|
||||
|
||||
Opponent = StaticOpponent | SelfPlayOpponent
|
||||
|
||||
|
||||
|
|
@ -50,36 +30,15 @@ OPPONENT_TYPES: dict[str, type[Agent]] = {
|
|||
}
|
||||
|
||||
|
||||
def make_opponent(spec: str) -> Opponent:
|
||||
"""Create an opponent strategy from a single configuration string.
|
||||
|
||||
The spec is either a bare opponent name ("random", "expander", "hunter",
|
||||
"self_play") or a ``type:checkpoint`` pair for a model opponent, e.g.
|
||||
``"model:ppo_model.eqx"``.
|
||||
"""
|
||||
name, sep, checkpoint = spec.partition(":")
|
||||
def make_opponent(name: str) -> Opponent:
|
||||
"""Create an opponent strategy by configuration name."""
|
||||
normalized_name = name.lower().replace("-", "_")
|
||||
if normalized_name == "self_play":
|
||||
return SelfPlayOpponent()
|
||||
|
||||
if normalized_name == "model":
|
||||
if not sep:
|
||||
raise ValueError(
|
||||
"opponent 'model' requires a checkpoint path, e.g. 'model:ppo_model.eqx'"
|
||||
)
|
||||
# The key only seeds the network structure; deserialization overwrites
|
||||
# every leaf, so a fixed seed is sufficient.
|
||||
network = PolicyValueNetwork(jrandom.PRNGKey(0), in_channels=14)
|
||||
network = eqx.tree_deserialise_leaves(checkpoint, network)
|
||||
return ModelOpponent(network)
|
||||
|
||||
if sep:
|
||||
choices = ", ".join([*sorted(OPPONENT_TYPES), "model", "self_play"])
|
||||
raise ValueError(f"opponent {spec!r} does not take a checkpoint; choose one of: {choices}")
|
||||
|
||||
try:
|
||||
opponent_type = OPPONENT_TYPES[normalized_name]
|
||||
except KeyError as error:
|
||||
choices = ", ".join([*sorted(OPPONENT_TYPES), "model", "self_play"])
|
||||
raise ValueError(f"unknown opponent {spec!r}; choose one of: {choices}") from error
|
||||
choices = ", ".join([*sorted(OPPONENT_TYPES), "self_play"])
|
||||
raise ValueError(f"unknown opponent {name!r}; choose one of: {choices}") from error
|
||||
return opponent_type()
|
||||
|
|
|
|||
BIN
tests/__pycache__/test_checkpoint.cpython-313-pytest-9.1.1.pyc
Normal file
BIN
tests/__pycache__/test_checkpoint.cpython-313-pytest-9.1.1.pyc
Normal file
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BIN
tests/__pycache__/test_mcts.cpython-313-pytest-9.1.1.pyc
Normal file
BIN
tests/__pycache__/test_mcts.cpython-313-pytest-9.1.1.pyc
Normal file
Binary file not shown.
BIN
tests/__pycache__/test_opponents.cpython-313-pytest-9.1.1.pyc
Normal file
BIN
tests/__pycache__/test_opponents.cpython-313-pytest-9.1.1.pyc
Normal file
Binary file not shown.
BIN
tests/__pycache__/test_self_play.cpython-313-pytest-9.1.1.pyc
Normal file
BIN
tests/__pycache__/test_self_play.cpython-313-pytest-9.1.1.pyc
Normal file
Binary file not shown.
BIN
tests/__pycache__/test_train_config.cpython-313-pytest-9.1.1.pyc
Normal file
BIN
tests/__pycache__/test_train_config.cpython-313-pytest-9.1.1.pyc
Normal file
Binary file not shown.
BIN
tests/__pycache__/test_training.cpython-313-pytest-9.1.1.pyc
Normal file
BIN
tests/__pycache__/test_training.cpython-313-pytest-9.1.1.pyc
Normal file
Binary file not shown.
|
|
@ -1,7 +1,7 @@
|
|||
import pytest
|
||||
from generals.agents import ExpanderAgent, HunterAgent, RandomAgent
|
||||
|
||||
from general_bots_training.opponents import ModelOpponent, SelfPlayOpponent, make_opponent
|
||||
from general_bots_training.opponents import SelfPlayOpponent, make_opponent
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
|
@ -28,28 +28,3 @@ def test_make_opponent_supports_self_play_aliases():
|
|||
def test_make_opponent_rejects_unknown_name():
|
||||
with pytest.raises(ValueError, match="unknown opponent"):
|
||||
make_opponent("turtle")
|
||||
|
||||
|
||||
def test_make_opponent_model_requires_checkpoint():
|
||||
with pytest.raises(ValueError, match="requires a checkpoint path"):
|
||||
make_opponent("model")
|
||||
|
||||
|
||||
def test_make_opponent_model_loads_checkpoint(tmp_path):
|
||||
import equinox as eqx
|
||||
import jax.random as jrandom
|
||||
|
||||
from general_bots_training.network import PolicyValueNetwork
|
||||
|
||||
network = PolicyValueNetwork(jrandom.PRNGKey(0), in_channels=14)
|
||||
checkpoint_path = tmp_path / "model.eqx"
|
||||
eqx.tree_serialise_leaves(checkpoint_path, network)
|
||||
|
||||
opponent = make_opponent(f"model:{checkpoint_path}")
|
||||
|
||||
assert isinstance(opponent, ModelOpponent)
|
||||
|
||||
|
||||
def test_make_opponent_rejects_checkpoint_on_non_model():
|
||||
with pytest.raises(ValueError, match="does not take a checkpoint"):
|
||||
make_opponent("random:some.eqx")
|
||||
|
|
|
|||
222
uv.lock
generated
222
uv.lock
generated
|
|
@ -132,20 +132,12 @@ source = { editable = "." }
|
|||
dependencies = [
|
||||
{ name = "equinox" },
|
||||
{ name = "generals-bots" },
|
||||
{ name = "jax" },
|
||||
{ name = "jax", extra = ["cuda"] },
|
||||
{ name = "jaxtyping" },
|
||||
{ name = "omegaconf" },
|
||||
{ name = "optax" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
cuda = [
|
||||
{ name = "jax", extra = ["cuda"] },
|
||||
]
|
||||
cuda13 = [
|
||||
{ name = "jax", extra = ["cuda13"] },
|
||||
]
|
||||
|
||||
[package.dev-dependencies]
|
||||
dev = [
|
||||
{ name = "ipython" },
|
||||
|
|
@ -156,14 +148,11 @@ dev = [
|
|||
requires-dist = [
|
||||
{ name = "equinox", specifier = ">=0.13.8" },
|
||||
{ name = "generals-bots", git = "https://github.com/strakam/generals-bots.git" },
|
||||
{ name = "jax", specifier = "==0.11.0" },
|
||||
{ name = "jax", extras = ["cuda"], marker = "extra == 'cuda'", specifier = "==0.11.0" },
|
||||
{ name = "jax", extras = ["cuda13"], marker = "extra == 'cuda13'", specifier = "==0.11.0" },
|
||||
{ name = "jax", extras = ["cuda"], specifier = ">=0.11.0" },
|
||||
{ name = "jaxtyping", specifier = ">=0.3.11" },
|
||||
{ name = "omegaconf", specifier = ">=2.3.1" },
|
||||
{ name = "optax", specifier = ">=0.2.8" },
|
||||
]
|
||||
provides-extras = ["cuda", "cuda13"]
|
||||
|
||||
[package.metadata.requires-dev]
|
||||
dev = [
|
||||
|
|
@ -264,10 +253,6 @@ cuda = [
|
|||
{ name = "jax-cuda12-plugin", extra = ["with-cuda"] },
|
||||
{ name = "jaxlib" },
|
||||
]
|
||||
cuda13 = [
|
||||
{ name = "jax-cuda13-plugin", extra = ["with-cuda"] },
|
||||
{ name = "jaxlib" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jax-cuda12-pjrt"
|
||||
|
|
@ -310,48 +295,6 @@ with-cuda = [
|
|||
{ name = "nvidia-nvshmem-cu12", marker = "sys_platform == 'linux'" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jax-cuda13-pjrt"
|
||||
version = "0.11.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/dd/cd/8fa8034e2f7bc50a78c2319f576aedf06c633b94b3e5d187fb9becfc1379/jax_cuda13_pjrt-0.11.0-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:e72c7b6b266b3fbe85792b2b4569b6c87447e77c454770d5afcc081a0b87d140", size = 120632519, upload-time = "2026-07-16T18:58:54.921Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/6e/0e/140944fc5cc1e0d5891b78209fb70a2f1ce951a656292f598904ac7efe5d/jax_cuda13_pjrt-0.11.0-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:814092a06a4da93a0b01df9bcdf5f8e0900d6c0a4c3ca7e7ec4ec967de7e96d9", size = 125815692, upload-time = "2026-07-16T18:58:59.251Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jax-cuda13-plugin"
|
||||
version = "0.11.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
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{ name = "jax-cuda13-pjrt" },
|
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]
|
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wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/19/59/e744e3362e8139e497b1a0c1bb27666ff7b1a4a4e20007b5105afc27deaf/jax_cuda13_plugin-0.11.0-cp313-cp313-manylinux_2_27_aarch64.whl", hash = "sha256:7d11c242dc94cd24c44ce0b67f49d5771040a5e83b594b31b9ad42e88fb8821b", size = 7547142, upload-time = "2026-07-16T18:59:06.604Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/06/14/74da5a18490a7d24bdc21dedf90b20eab4e4658879f333bea9772f36291b/jax_cuda13_plugin-0.11.0-cp314-cp314-manylinux_2_27_aarch64.whl", hash = "sha256:d9147b3db90f140bd94afb29589a7eaeb7fea1b11159420394c60d466ea9a7c1", size = 7547841, upload-time = "2026-07-16T18:59:10.03Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/42/35/9131f3182b4ef67dce4845c697c8cab79bc0df8ffb15e7806bd099cae4e3/jax_cuda13_plugin-0.11.0-cp314-cp314-manylinux_2_27_x86_64.whl", hash = "sha256:09be21c598788f0b714f27cc94e3ff8a169b7854b6db883287d6cbc6d4d8d911", size = 7571369, upload-time = "2026-07-16T18:59:11.811Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/29/67/7f057e2533f14fcdfa236deb2272e202549fd0a1b461c65d8f9c93fc2c6c/jax_cuda13_plugin-0.11.0-cp314-cp314t-manylinux_2_27_aarch64.whl", hash = "sha256:87d847502f03e7ce37e6ef570245fca1323af9a7c0b86ffb615f794e24525d70", size = 7561528, upload-time = "2026-07-16T18:59:13.288Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/3f/27/1ada0a898c34d009478814fb5a1c2c73cb59036d68563992a1ee0dfcadde/jax_cuda13_plugin-0.11.0-cp314-cp314t-manylinux_2_27_x86_64.whl", hash = "sha256:8f9e14c1b7ef52cccf62594fecd0cde2bb150a0f933e831b0b66b2cb27d91000", size = 7580472, upload-time = "2026-07-16T18:59:14.734Z" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
with-cuda = [
|
||||
{ name = "nvidia-cublas", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-cuda-nvcc", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-cudnn-cu13", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-cufft", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-cusolver", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-cusparse", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-nccl-cu13", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-nvjitlink", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-nvshmem-cu13", marker = "sys_platform == 'linux'" },
|
||||
{ name = "nvidia-nvvm" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jaxlib"
|
||||
version = "0.11.0"
|
||||
|
|
@ -482,18 +425,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/a1/5a/4d2b1601df3602dba7a14f3348ba9bfe94a18adb428e693df6154c293831/numpy-2.5.1-cp314-cp314t-win_arm64.whl", hash = "sha256:5a6db61f9aaa57e369905c67d852045d3c4f7126405b29d09b19dec118e9c9cb", size = 10697674, upload-time = "2026-07-04T17:07:58.506Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cublas"
|
||||
version = "13.6.1.10"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "nvidia-cuda-nvrtc" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/42/51/a174a0e79793e528ef4645a9d554e3aa48fd8da3f9c9cf523c0176bb121b/nvidia_cublas-13.6.1.10-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:e05a431062a17cb9b02e2f37e67817b637051ce8fad57b388482c594396ddbb4", size = 518610411, upload-time = "2026-07-30T18:18:32.969Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/dd/e9/288a93d8234b8f8ea0ccac7b8cc5d7aa4c663d9676c64f4a2eb3a1f9ffc4/nvidia_cublas-13.6.1.10-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:feb2ed8a1e211bc5774413efc0f1a08c4d5269b56f68b4ac6fe5408e57f7dc1c", size = 410475904, upload-time = "2026-07-30T18:19:26.388Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cublas-cu12"
|
||||
version = "12.9.2.10"
|
||||
|
|
@ -506,15 +437,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/cb/c0/0a517bfe63ccd3b92eb254d264e28fca3c7cab75d07daea315250fb1bf73/nvidia_cublas_cu12-12.9.2.10-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:e4f53a8ca8c5d6e8c492d0d0a3d565ecb59a751b19cfdaa4f6da0ab2104c1702", size = 581240110, upload-time = "2026-04-08T18:52:31.532Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cuda-cccl"
|
||||
version = "13.3.3.4.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/96/bd/572971ffc14bd36676c821fc15d991b08fe6179cb09368250147475f954d/nvidia_cuda_cccl-13.3.3.4.1-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:067d19b4b3c9d0f2ebec9f29a311b2863db96bf98e058bbc331597d51ce818cf", size = 3454030, upload-time = "2026-06-29T16:41:49.092Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/f8/ab/049726d90147865a3ea53bae6cb7c35b98bf1fdf96cdb967101329625f83/nvidia_cuda_cccl-13.3.3.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cc0adc188d570b09f4d606c7dc05a42aa3d8aa082e0d60f7bbfc5b6435f627c6", size = 3454034, upload-time = "2026-06-29T16:42:07.435Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cuda-cccl-cu12"
|
||||
version = "12.9.27"
|
||||
|
|
@ -524,24 +446,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/18/2a/d4cd8506d2044e082f8cd921be57392e6a9b5ccd3ffdf050362430a3d5d5/nvidia_cuda_cccl_cu12-12.9.27-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:37869e17ce2e1ecec6eddf1927cca0f8c34e64fd848d40453df559091e2d7117", size = 3152243, upload-time = "2025-05-01T19:32:13.955Z" },
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cuda-crt"
|
||||
version = "13.3.73"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fa/41/2089e411507d66458d67208bdd1bc562d492bb6458c3d2aea4603072a219/nvidia_cuda_crt-13.3.73-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:60aacc0b5e1e8b40c62abe4d1ab16440add91b99bd2f17f62dd091586b73d166", size = 157353, upload-time = "2026-06-29T16:42:38.163Z" },
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{ url = "https://files.pythonhosted.org/packages/7e/ce/16d76f4b5b3f7460f5ebd17516685495c149c66651ffdd381f90e4d4e65c/nvidia_cuda_crt-13.3.73-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:df14a17ae1c5c3171265411212246654d780f89344ea85344466c6b955247543", size = 157352, upload-time = "2026-06-29T16:43:09.209Z" },
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|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cuda-cupti"
|
||||
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|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/d1/7b/d332e362c2c48929f913a5930a596c707c30632ae3248dd16be35f18fc11/nvidia_cuda_cupti-13.3.75-py3-none-manylinux_2_25_aarch64.whl", hash = "sha256:79f5a8c55378a998427d6974b1d85ec91ef684035d016b3fb3b85006914f94f0", size = 11824795, upload-time = "2026-06-29T16:46:00.894Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/d1/68/ee003f64d79bd3cb95e243cfdc906bb0f1ae73b1aa2857baf1d7834dd1ee/nvidia_cuda_cupti-13.3.75-py3-none-manylinux_2_25_x86_64.whl", hash = "sha256:a969cc446c44db612e417bd0af722cfad9009c027dabd4ed255262c0e9a9a3d2", size = 12044533, upload-time = "2026-06-29T16:46:20.662Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cuda-cupti-cu12"
|
||||
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|
||||
|
|
@ -551,20 +455,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/c1/2e/b84e32197e33f39907b455b83395a017e697c07a449a2b15fd07fc1c9981/nvidia_cuda_cupti_cu12-12.9.79-py3-none-manylinux_2_25_x86_64.whl", hash = "sha256:096bcf334f13e1984ba36685ad4c1d6347db214de03dbb6eebb237b41d9d934f", size = 10814997, upload-time = "2025-06-05T20:01:10.168Z" },
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cuda-nvcc"
|
||||
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|
||||
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|
||||
dependencies = [
|
||||
{ name = "nvidia-cuda-crt" },
|
||||
{ name = "nvidia-cuda-runtime" },
|
||||
{ name = "nvidia-nvvm" },
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||||
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|
||||
wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/5c/14/9f5cdc994d5431e2f08f62ffe34509e7feabd1f2e18517e2d7720c6ff0fd/nvidia_cuda_nvcc-13.3.73-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:70f250825355d2c3aa6c7a972a0ec00f020bad66d2679e527eb4336301c904aa", size = 39515578, upload-time = "2026-06-29T16:47:40.318Z" },
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{ url = "https://files.pythonhosted.org/packages/83/19/e46ef3597ba47a9f8a91ab24533db42a600b659fc418dbe4af0b630bcb41/nvidia_cuda_nvcc-13.3.73-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f483af83166c4fa356a21606076d553b0b4ceaebbd9912e537545080db695bdd", size = 44942138, upload-time = "2026-06-29T16:48:13.615Z" },
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||||
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|
||||
[[package]]
|
||||
name = "nvidia-cuda-nvcc-cu12"
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|
@ -574,15 +464,6 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/d6/5c/8cc072436787104bbbcbde1f76ab4a0d89e68f7cebc758dd2ad7913a43d0/nvidia_cuda_nvcc_cu12-12.9.86-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:44e1eca4d08926193a558d2434b1bf83d57b4d5743e0c431c0c83d51da1df62b", size = 39411138, upload-time = "2025-06-05T20:01:43.182Z" },
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[[package]]
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{ url = "https://files.pythonhosted.org/packages/e7/b6/60a3641111d39ebfcfcd8b8bfd0290d7623c4b8b5f90952c2d84776f8ca4/nvidia_cuda_nvrtc-13.3.33-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7b05ecda494c6dabc44231a608b060a71008a730d9dfda932cc508e6d29159e0", size = 49260054, upload-time = "2026-05-26T16:37:51.177Z" },
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@ -592,15 +473,6 @@ wheels = [
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|||
Loading…
Reference in a new issue