.gitignore

@@ -1,2 +0,0 @@
-__pycache__/
-competition/.venv-competition/

Justfile

@@ -1,2 +0,0 @@
-vendor:
-    pip download -r competition/requirements-vendor.txt -d competition/wheelhouse/ --python-version 3.12 --platform manylinux --no-deps

competition/build.sh

@@ -1,7 +0,0 @@
-#!/usr/bin/env bash
-# One-time intake step: install the vendored wheels offline.
-# The sandbox has no network, so everything must come from wheelhouse/.
-set -euo pipefail
-cd "$(dirname "$0")"
-python -m pip install --no-index --find-links=wheelhouse -r requirements-vendor.txt
-echo "[build] vendored wheels installed" >&2

competition/competition.py

@@ -1,336 +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")
-    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()

competition/requirements-vendor.txt

@@ -1,3 +0,0 @@
-equinox==0.13.8
-jaxtyping==0.3.11
-wadler-lindig==0.1.7

competition/run.sh

@@ -1,8 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-# Competition entrypoint. The sandbox runs `run.sh` from the submission root;
-# cd to our own directory so relative paths work regardless of the cwd.
-# `python` is the sandbox's CPython 3.12 with jax/numpy pre-installed; equinox
-# and its deps are installed by build.sh from wheelhouse/.
-cd "$(dirname "$0")"
-exec python -u competition.py model.eqx

pyproject.toml

@@ -5,20 +5,12 @@ requires-python = ">=3.13"
 dependencies = [
     "equinox>=0.13.8",
     "generals-bots",
-    "jax==0.11.0",
+    "jax[cuda]>=0.11.0",
     "jaxtyping>=0.3.11",
     "omegaconf>=2.3.1",
     "optax>=0.2.8",
 ]
 
-[project.optional-dependencies]
-cuda = [
-    "jax[cuda]==0.11.0"
-]
-cuda13 = [
-    "jax[cuda13]==0.11.0"
-]
-
 [tool.uv.sources]
 generals-bots = { git = "https://github.com/strakam/generals-bots.git" }
 

requirements-competition.txt

@@ -1,11 +0,0 @@
-numpy==2.4.6
-scipy==1.18.0
-pandas==3.0.5
-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
-networkx==3.6.1
-safetensors==0.8.0
-gymnasium==1.3.0
-# torch==2.13.0

requirements-vendor.txt

@@ -1,3 +0,0 @@
-equinox==0.13.8
-jaxtyping==0.3.11
-wadler-lindig==0.1.7

requirements.txt

@@ -1,81 +0,0 @@
-# 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

scripts/competition.py

@@ -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()

scripts/competition_puct.py

@@ -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()

scripts/evaluate.py

@@ -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())

scripts/run.sh

@@ -1,4 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-cd "$(dirname "$0")"
-exec python -u competition.py ../ppo_model.eqx

src/general_bots_training/__init__.py

@@ -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",
 ]

src/general_bots_training/opponents.py

@@ -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:
+def make_opponent(name: str) -> Opponent:
-    """Create an opponent strategy from a single configuration string.
+    """Create an opponent strategy by configuration name."""
-
-    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(":")
     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"])
+        choices = ", ".join([*sorted(OPPONENT_TYPES), "self_play"])
-        raise ValueError(f"unknown opponent {spec!r}; choose one of: {choices}") from error
+        raise ValueError(f"unknown opponent {name!r}; choose one of: {choices}") from error
     return opponent_type()

tests/test_opponents.py

@@ -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")

uv.lock

@@ -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"], 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 = "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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