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Deep CFR YAML config 추가

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This commit is contained in:
coolguy
2026-05-07 00:30:42 +09:00
parent d86e534ba9
commit f593a6d910
11 changed files with 610 additions and 267 deletions
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configs/deep_cfr/smoke.yaml
+35
View File
@@ -0,0 +1,35 @@
run:
iterations: 1
seed: 1
device: cpu
network:
hidden_size: 16
traversal:
traversals_per_iteration: 1
max_depth: 2
max_nodes: 64
num_workers: 0
worker_chunk_size: 1
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 2
learning_rate: 0.001
memory:
advantage_capacity: 1000
strategy_capacity: 1000
checkpoint:
directory: runs/deep_cfr/smoke
save_every_iteration: false
evaluation:
eval_every: 0
games: 2
opponents:
- random
max_steps: 10000
docs/deep-cfr-v0-gap-vs-coolrl.md
+2 -2
View File
@@ -97,8 +97,8 @@ extras.
Still smaller than legacy:
1. Config is a single `DeepCFRConfig` dataclass rather than a deeply nested
YAML-first config tree.
1. Config is YAML-first and nested through Pydantic, but only one smoke preset
exists under `configs/deep_cfr/`.
2. Multiprocessing exists, but it is intentionally simple:
- no progress callback per worker batch
- no hotspot timing profile
pyproject.toml
+2
View File
@@ -9,6 +9,8 @@ authors = [
requires-python = ">=3.11"
dependencies = [
"numpy>=1.26.0",
"pydantic>=2.7",
"PyYAML>=6.0",
"torch>=2.3.0",
]
src/coolrl_lost_cities/games/classic/deep_cfr/benchmark.py
+12 -8
View File
@@ -1,7 +1,6 @@
from __future__ import annotations
import time
from dataclasses import replace
from coolrl_lost_cities.games.classic.deep_cfr.config import DeepCFRConfig
from coolrl_lost_cities.games.classic.deep_cfr.trainer import DeepCFRTrainer
@@ -13,14 +12,19 @@ def benchmark_traversal(
*,
num_workers: int = 0,
) -> dict[str, float | int]:
base = config or DeepCFRConfig(
iterations=1,
traversals_per_iteration=8,
max_traversal_depth=4,
save_every_iteration=False,
base = config or DeepCFRConfig.model_validate(
{
"run": {"iterations": 1},
"traversal": {"traversals_per_iteration": 8, "max_depth": 4},
"checkpoint": {"save_every_iteration": False},
}
)
cfg = replace(base, num_workers=num_workers, save_every_iteration=False, eval_every=0)
trainer = DeepCFRTrainer(cfg, classic_config(seed=cfg.seed))
data = base.model_dump(mode="python")
data["traversal"]["num_workers"] = num_workers
data["checkpoint"]["save_every_iteration"] = False
data["evaluation"]["eval_every"] = 0
cfg = DeepCFRConfig.model_validate(data)
trainer = DeepCFRTrainer(cfg, classic_config(seed=cfg.run.seed))
started = time.perf_counter()
metrics = trainer.run_iteration(1)
elapsed = time.perf_counter() - started
src/coolrl_lost_cities/games/classic/deep_cfr/cli.py
+49 -25
View File
@@ -2,14 +2,13 @@ from __future__ import annotations
import argparse
import json
from dataclasses import replace
from pathlib import Path
from typing import Any
from coolrl_lost_cities.games.classic.deep_cfr.benchmark import (
benchmark_traversal,
benchmark_traversal_modes,
)
from coolrl_lost_cities.games.classic.deep_cfr.config import DeepCFRConfig, config_from_dict
from coolrl_lost_cities.games.classic.deep_cfr.config import DeepCFRConfig, load_config
from coolrl_lost_cities.games.classic.deep_cfr.evaluate import (
evaluate_strategy_network,
load_strategy_policy_from_checkpoint,
@@ -27,27 +26,48 @@ from coolrl_lost_cities.games.classic.game import classic_config
def _load_config(path: str | None) -> DeepCFRConfig:
if path is None:
return DeepCFRConfig()
return config_from_dict(json.loads(Path(path).read_text(encoding="utf-8")))
return load_config(path)
def _deep_update(base: dict[str, Any], patch: dict[str, Any]) -> None:
for key, value in patch.items():
if isinstance(value, dict) and isinstance(base.get(key), dict):
_deep_update(base[key], value)
else:
base[key] = value
def _with_overrides(config: DeepCFRConfig, overrides: dict[str, Any]) -> DeepCFRConfig:
data = config.model_dump(mode="python")
_deep_update(data, overrides)
return DeepCFRConfig.model_validate(data)
def train_command(args: argparse.Namespace) -> None:
config = _load_config(args.config)
overrides = {}
for key in (
"iterations",
"traversals_per_iteration",
"checkpoint_dir",
"eval_every",
"eval_games",
"seed",
):
value = getattr(args, key)
if value is not None:
overrides[key] = value
overrides: dict[str, Any] = {}
if args.iterations is not None:
overrides.setdefault("run", {})["iterations"] = args.iterations
if args.seed is not None:
overrides.setdefault("run", {})["seed"] = args.seed
if args.traversals_per_iteration is not None:
overrides.setdefault("traversal", {})["traversals_per_iteration"] = (
args.traversals_per_iteration
)
if args.checkpoint_dir is not None:
overrides.setdefault("checkpoint", {})["directory"] = args.checkpoint_dir
if args.eval_every is not None:
overrides.setdefault("evaluation", {})["eval_every"] = args.eval_every
if args.eval_games is not None:
overrides.setdefault("evaluation", {})["games"] = args.eval_games
if args.no_save:
overrides["save_every_iteration"] = False
config = replace(config, **overrides)
trainer = DeepCFRTrainer(config, classic_config(seed=config.seed), device=args.device)
overrides.setdefault("checkpoint", {})["save_every_iteration"] = False
config = _with_overrides(config, overrides)
trainer = DeepCFRTrainer(
config,
classic_config(seed=config.run.seed),
device=args.device or config.run.device,
)
if args.resume:
trainer.load_checkpoint(args.resume)
metrics = trainer.train()
@@ -70,11 +90,15 @@ def eval_command(args: argparse.Namespace) -> None:
def benchmark_command(args: argparse.Namespace) -> None:
config = DeepCFRConfig(
traversals_per_iteration=args.traversals,
max_traversal_depth=args.depth,
seed=args.seed,
save_every_iteration=False,
config = DeepCFRConfig.model_validate(
{
"run": {"seed": args.seed},
"traversal": {
"traversals_per_iteration": args.traversals,
"max_depth": args.depth,
},
"checkpoint": {"save_every_iteration": False},
}
)
if args.compare:
print(json.dumps(benchmark_traversal_modes(config), indent=2, sort_keys=True))
@@ -135,7 +159,7 @@ def main(argv: list[str] | None = None) -> None:
train.add_argument("--traversals-per-iteration", type=int)
train.add_argument("--checkpoint-dir")
train.add_argument("--resume")
train.add_argument("--device", default="cpu")
train.add_argument("--device")
train.add_argument("--eval-every", type=int)
train.add_argument("--eval-games", type=int)
train.add_argument("--seed", type=int)
src/coolrl_lost_cities/games/classic/deep_cfr/config.py
+116 -42
View File
@@ -1,16 +1,33 @@
from __future__ import annotations
from dataclasses import asdict, dataclass
import json
import os
from collections.abc import Mapping
from pathlib import Path
from typing import Any
import yaml
from pydantic import BaseModel, ConfigDict, Field, field_validator
@dataclass(frozen=True)
class DeepCFRConfig:
class StrictModel(BaseModel):
model_config = ConfigDict(extra="forbid")
class RunConfig(StrictModel):
iterations: int = 1
seed: int = 1
device: str = "cpu"
class NetworkConfig(StrictModel):
hidden_size: int = 64
class TraversalConfig(StrictModel):
traversals_per_iteration: int = 2
max_traversal_depth: int | None = 8
max_nodes_per_traversal: int | None = 10_000
max_depth: int | None = 8
max_nodes: int | None = 10_000
regret_matching_epsilon: float = 1.0e-8
outcome_sampling_epsilon: float = 0.0
outcome_sampling_value_clip: float | None = None
@@ -20,59 +37,116 @@ class DeepCFRConfig:
cutoff_rollout_policy: str = "random"
cutoff_rollout_max_steps: int = 10_000
opponent_policy: str = "network"
self_play_snapshot_every: int = 1
self_play_max_snapshots: int = 20
self_play_anchor_probability: float = 0.0
self_play_current_weight: float = 0.5
self_play_recent_weight: float = 0.3
self_play_older_weight: float = 0.2
self_play_anchor_weight: float = 0.0
self_play_recent_window: int = 5
strategy_sample_interval: int = 1
store_strategy_on_traverser_nodes: bool = True
store_strategy_on_opponent_nodes: bool = True
advantage_memory_capacity: int = 2_000_000
strategy_memory_capacity: int = 2_000_000
advantage_train_steps: int = 1
strategy_train_steps: int = 1
batch_size: int = 32
hidden_size: int = 64
learning_rate: float = 1.0e-3
seed: int = 1
checkpoint_dir: str = "runs/deep_cfr/default"
save_every_iteration: bool = True
eval_every: int = 0
eval_games: int = 10
eval_opponents: tuple[str, ...] = ("random",)
eval_max_steps: int = 10_000
num_workers: int | str = 0
traversal_worker_chunk_size: int = 4
worker_chunk_size: int = 4
def to_dict(self) -> dict[str, Any]:
return asdict(self)
@field_validator("outcome_unsampled_regret")
@classmethod
def _validate_unsampled_regret(cls, value: str) -> str:
if value not in {"negative_node_value", "zero"}:
raise ValueError("must be 'negative_node_value' or 'zero'")
return value
@property
def checkpoint_path(self) -> Path:
return Path(self.checkpoint_dir)
@field_validator("cutoff_value_mode")
@classmethod
def _validate_cutoff_value_mode(cls, value: str) -> str:
if value not in {"score_diff", "random_rollout"}:
raise ValueError("must be 'score_diff' or 'random_rollout'")
return value
@field_validator("cutoff_rollout_policy")
@classmethod
def _validate_cutoff_rollout_policy(cls, value: str) -> str:
if value not in {"random", "safe_heuristic"}:
raise ValueError("must be 'random' or 'safe_heuristic'")
return value
@field_validator("opponent_policy")
@classmethod
def _validate_opponent_policy(cls, value: str) -> str:
if value not in {"network", "safe_heuristic", "self_play_league"}:
raise ValueError("must be 'network', 'safe_heuristic', or 'self_play_league'")
return value
def resolved_num_workers(self, batches: int | None = None) -> int:
if isinstance(self.num_workers, str):
token = self.num_workers.strip().lower()
if token == "auto":
guess = max(1, (os_cpu_count() or 2) // 2)
guess = max(1, (os.cpu_count() or 2) // 2)
return min(guess, batches) if batches is not None and batches > 0 else guess
return max(0, int(token))
return max(0, int(self.num_workers))
def config_from_dict(data: dict[str, Any]) -> DeepCFRConfig:
values = dict(data)
if "eval_opponents" in values:
values["eval_opponents"] = tuple(values["eval_opponents"])
return DeepCFRConfig(**values)
class SelfPlayLeagueConfig(StrictModel):
snapshot_every: int = 1
max_snapshots: int = 20
anchor_probability: float = 0.0
current_weight: float = 0.5
recent_weight: float = 0.3
older_weight: float = 0.2
anchor_weight: float = 0.0
recent_window: int = 5
def os_cpu_count() -> int | None:
import os
class OptimizationConfig(StrictModel):
advantage_train_steps: int = 1
strategy_train_steps: int = 1
batch_size: int = 32
learning_rate: float = 1.0e-3
return os.cpu_count()
class MemoryConfig(StrictModel):
advantage_capacity: int = 2_000_000
strategy_capacity: int = 2_000_000
class CheckpointConfig(StrictModel):
directory: str = "runs/deep_cfr/default"
save_every_iteration: bool = True
@property
def path(self) -> Path:
return Path(self.directory)
class EvaluationConfig(StrictModel):
eval_every: int = 0
games: int = 10
opponents: tuple[str, ...] = ("random",)
max_steps: int = 10_000
class DeepCFRConfig(StrictModel):
run: RunConfig = Field(default_factory=RunConfig)
network: NetworkConfig = Field(default_factory=NetworkConfig)
traversal: TraversalConfig = Field(default_factory=TraversalConfig)
self_play: SelfPlayLeagueConfig = Field(default_factory=SelfPlayLeagueConfig)
optimization: OptimizationConfig = Field(default_factory=OptimizationConfig)
memory: MemoryConfig = Field(default_factory=MemoryConfig)
checkpoint: CheckpointConfig = Field(default_factory=CheckpointConfig)
evaluation: EvaluationConfig = Field(default_factory=EvaluationConfig)
def to_dict(self) -> dict[str, Any]:
return self.model_dump(mode="json")
@property
def checkpoint_path(self) -> Path:
return self.checkpoint.path
def config_from_dict(data: Mapping[str, Any]) -> DeepCFRConfig:
return DeepCFRConfig.model_validate(data)
def load_config(path: str | Path) -> DeepCFRConfig:
config_path = Path(path)
text = config_path.read_text(encoding="utf-8")
if config_path.suffix.lower() in {".yaml", ".yml"}:
data = yaml.safe_load(text) or {}
else:
data = json.loads(text)
return config_from_dict(data)
src/coolrl_lost_cities/games/classic/deep_cfr/evaluate.py
+1 -1
View File
@@ -97,7 +97,7 @@ def load_strategy_policy_from_checkpoint(
network = DeepCFRMLP(
int(payload["input_dim"]),
int(payload["action_size"]),
cfg.hidden_size,
cfg.network.hidden_size,
).to(device)
network.load_state_dict(payload["strategy_network"])
network.eval()
src/coolrl_lost_cities/games/classic/deep_cfr/trainer.py
+65 -56
View File
@@ -68,31 +68,33 @@ class DeepCFRTrainer:
device: str = "cpu",
) -> None:
self.config = config or DeepCFRConfig()
self.game_config = game_config or LostCitiesConfig(seed=self.config.seed)
self.game_config = game_config or LostCitiesConfig(seed=self.config.run.seed)
self.device = torch.device(device)
probe = GameState.new_game(self.game_config, seed=self.config.seed)
probe = GameState.new_game(self.game_config, seed=self.config.run.seed)
self.input_dim = input_dim(probe)
self.action_size = 2 * probe.config.hand_size + 1 + probe.config.n_colors
torch.manual_seed(self.config.seed)
torch.manual_seed(self.config.run.seed)
self.advantage_networks = [
DeepCFRMLP(self.input_dim, self.action_size, self.config.hidden_size).to(self.device)
DeepCFRMLP(self.input_dim, self.action_size, self.config.network.hidden_size).to(
self.device
)
for _ in range(2)
]
self.strategy_network = DeepCFRMLP(
self.input_dim, self.action_size, self.config.hidden_size
self.input_dim, self.action_size, self.config.network.hidden_size
).to(self.device)
self.advantage_optimizers = [
torch.optim.Adam(network.parameters(), lr=self.config.learning_rate)
torch.optim.Adam(network.parameters(), lr=self.config.optimization.learning_rate)
for network in self.advantage_networks
]
self.strategy_optimizer = torch.optim.Adam(
self.strategy_network.parameters(), lr=self.config.learning_rate
self.strategy_network.parameters(), lr=self.config.optimization.learning_rate
)
self.advantage_memory = ReservoirMemory(self.config.advantage_memory_capacity)
self.strategy_memory = ReservoirMemory(self.config.strategy_memory_capacity)
self.rng = np.random.default_rng(self.config.seed + 101)
self.advantage_memory = ReservoirMemory(self.config.memory.advantage_capacity)
self.strategy_memory = ReservoirMemory(self.config.memory.strategy_capacity)
self.rng = np.random.default_rng(self.config.run.seed + 101)
self.iteration = 0
self.run_dir = self.config.checkpoint_path
self.metrics_path = self.run_dir / "metrics.jsonl"
@@ -138,7 +140,7 @@ class DeepCFRTrainer:
def run_iteration(self, iteration: int) -> IterationMetrics:
self.iteration = iteration
if self.config.resolved_num_workers() > 1:
if self.config.traversal.resolved_num_workers() > 1:
total_stats = self._run_traversals_parallel(iteration)
else:
total_stats = self._run_traversals_single_process(iteration)
@@ -168,34 +170,34 @@ class DeepCFRTrainer:
self.strategy_memory,
device=self.device,
action_size=self.action_size,
epsilon=self.config.regret_matching_epsilon,
strategy_sample_interval=self.config.strategy_sample_interval,
store_strategy_on_traverser_nodes=self.config.store_strategy_on_traverser_nodes,
store_strategy_on_opponent_nodes=self.config.store_strategy_on_opponent_nodes,
max_depth=self.config.max_traversal_depth,
max_nodes=self.config.max_nodes_per_traversal,
outcome_sampling_epsilon=self.config.outcome_sampling_epsilon,
outcome_sampling_value_clip=self.config.outcome_sampling_value_clip,
outcome_unsampled_regret=self.config.outcome_unsampled_regret,
cutoff_value_mode=self.config.cutoff_value_mode,
cutoff_rollouts=self.config.cutoff_rollouts,
cutoff_rollout_policy=self.config.cutoff_rollout_policy,
cutoff_rollout_max_steps=self.config.cutoff_rollout_max_steps,
opponent_policy=self.config.opponent_policy,
epsilon=self.config.traversal.regret_matching_epsilon,
strategy_sample_interval=self.config.traversal.strategy_sample_interval,
store_strategy_on_traverser_nodes=self.config.traversal.store_strategy_on_traverser_nodes,
store_strategy_on_opponent_nodes=self.config.traversal.store_strategy_on_opponent_nodes,
max_depth=self.config.traversal.max_depth,
max_nodes=self.config.traversal.max_nodes,
outcome_sampling_epsilon=self.config.traversal.outcome_sampling_epsilon,
outcome_sampling_value_clip=self.config.traversal.outcome_sampling_value_clip,
outcome_unsampled_regret=self.config.traversal.outcome_unsampled_regret,
cutoff_value_mode=self.config.traversal.cutoff_value_mode,
cutoff_rollouts=self.config.traversal.cutoff_rollouts,
cutoff_rollout_policy=self.config.traversal.cutoff_rollout_policy,
cutoff_rollout_max_steps=self.config.traversal.cutoff_rollout_max_steps,
opponent_policy=self.config.traversal.opponent_policy,
league_advantage_networks=self._materialize_league_networks(),
self_play_anchor_probability=self.config.self_play_anchor_probability,
self_play_current_weight=self.config.self_play_current_weight,
self_play_recent_weight=self.config.self_play_recent_weight,
self_play_older_weight=self.config.self_play_older_weight,
self_play_anchor_weight=self.config.self_play_anchor_weight,
self_play_recent_window=self.config.self_play_recent_window,
self_play_anchor_probability=self.config.self_play.anchor_probability,
self_play_current_weight=self.config.self_play.current_weight,
self_play_recent_weight=self.config.self_play.recent_weight,
self_play_older_weight=self.config.self_play.older_weight,
self_play_anchor_weight=self.config.self_play.anchor_weight,
self_play_recent_window=self.config.self_play.recent_window,
rng=self.rng,
)
for network in self.advantage_networks:
network.eval()
for traversal_index in range(self.config.traversals_per_iteration):
for traversal_index in range(self.config.traversal.traversals_per_iteration):
for player in range(2):
seed = self.config.seed + iteration * 10_000 + traversal_index * 10 + player
seed = self.config.run.seed + iteration * 10_000 + traversal_index * 10 + player
state = GameState.new_game(self.game_config, seed=seed)
_, stats = traverser.traverse(state, player, iteration)
total_stats.accumulate(stats)
@@ -206,7 +208,7 @@ class DeepCFRTrainer:
total_stats = TraversalStats()
if not batches:
return total_stats
max_workers = self.config.resolved_num_workers(len(batches))
max_workers = self.config.traversal.resolved_num_workers(len(batches))
with ProcessPoolExecutor(
max_workers=max_workers,
mp_context=mp.get_context("spawn"),
@@ -225,12 +227,12 @@ class DeepCFRTrainer:
{name: value.detach().cpu() for name, value in network.state_dict().items()}
for network in self.advantage_networks
]
chunk_size = max(1, self.config.traversal_worker_chunk_size)
chunk_size = max(1, self.config.traversal.worker_chunk_size)
batch_index = 0
for player in range(2):
seeds = [
self.config.seed + iteration * 10_000 + index * 10 + player
for index in range(self.config.traversals_per_iteration)
self.config.run.seed + iteration * 10_000 + index * 10 + player
for index in range(self.config.traversal.traversals_per_iteration)
]
for start in range(0, len(seeds), chunk_size):
chunk = seeds[start : start + chunk_size]
@@ -245,7 +247,7 @@ class DeepCFRTrainer:
action_size=self.action_size,
advantage_networks=network_payloads,
league_advantage_networks=self._league_payloads(),
worker_seed=self.config.seed + iteration * 1_000_003 + batch_index,
worker_seed=self.config.run.seed + iteration * 1_000_003 + batch_index,
)
)
batch_index += 1
@@ -258,14 +260,14 @@ class DeepCFRTrainer:
]
def _maybe_record_self_play_snapshot(self, iteration: int) -> None:
if self.config.opponent_policy != "self_play_league":
if self.config.traversal.opponent_policy != "self_play_league":
return
if self.config.self_play_max_snapshots <= 0:
if self.config.self_play.max_snapshots <= 0:
return
if iteration % max(1, self.config.self_play_snapshot_every) != 0:
if iteration % max(1, self.config.self_play.snapshot_every) != 0:
return
self.self_play_league_snapshots.append(self._frozen_advantage_state_dicts())
overflow = len(self.self_play_league_snapshots) - self.config.self_play_max_snapshots
overflow = len(self.self_play_league_snapshots) - self.config.self_play.max_snapshots
if overflow > 0:
del self.self_play_league_snapshots[:overflow]
@@ -273,7 +275,7 @@ class DeepCFRTrainer:
league: list[list[nn.Module]] = []
for snapshot in self.self_play_league_snapshots:
networks = [
DeepCFRMLP(self.input_dim, self.action_size, self.config.hidden_size).to(
DeepCFRMLP(self.input_dim, self.action_size, self.config.network.hidden_size).to(
self.device
)
for _ in range(2)
@@ -291,7 +293,7 @@ class DeepCFRTrainer:
self._start_run_logging()
metrics: list[IterationMetrics] = []
start = self.iteration + 1
stop = self.iteration + self.config.iterations
stop = self.iteration + self.config.run.iterations
for iteration in range(start, stop + 1):
started = time.perf_counter()
item = self.run_iteration(iteration)
@@ -299,7 +301,7 @@ class DeepCFRTrainer:
metrics.append(item)
self._append_metrics(item, elapsed)
self._maybe_record_self_play_snapshot(iteration)
if self.config.save_every_iteration:
if self.config.checkpoint.save_every_iteration:
checkpoint_dir = self.run_dir
self.save_checkpoint(checkpoint_dir / f"iteration_{iteration:05d}.pt", item)
self.save_checkpoint(checkpoint_dir / "latest.pt", item)
@@ -316,7 +318,9 @@ class DeepCFRTrainer:
if self.iteration == 0 and self.metrics_path.exists():
self.metrics_path.unlink()
with self.log_path.open("a", encoding="utf-8") as handle:
handle.write(f"Deep CFR run start iteration={self.iteration} seed={self.config.seed}\n")
handle.write(
f"Deep CFR run start iteration={self.iteration} seed={self.config.run.seed}\n"
)
def _append_metrics(self, metrics: IterationMetrics, iteration_seconds: float) -> None:
data = metrics.to_dict()
@@ -332,18 +336,21 @@ class DeepCFRTrainer:
)
def _evaluate(self, iteration: int) -> dict[str, float | int]:
if self.config.eval_every <= 0 or iteration % self.config.eval_every != 0:
if (
self.config.evaluation.eval_every <= 0
or iteration % self.config.evaluation.eval_every != 0
):
return {}
results: dict[str, float | int] = {}
for opponent in self.config.eval_opponents:
for opponent in self.config.evaluation.opponents:
result = evaluate_strategy_network(
self.strategy_network,
self.game_config,
games=self.config.eval_games,
seed=self.config.seed + iteration * 1000,
games=self.config.evaluation.games,
seed=self.config.run.seed + iteration * 1000,
opponent=opponent,
device=self.device,
max_steps=self.config.eval_max_steps,
max_steps=self.config.evaluation.max_steps,
)
for key, value in result.items():
results[f"eval_{opponent}_{key}"] = value
@@ -365,7 +372,7 @@ class DeepCFRTrainer:
return self._train_strategy(self.strategy_network, self.strategy_optimizer, samples)
def _batch(self, samples: list[TrainingSample], step: int) -> list[TrainingSample]:
batch_size = min(self.config.batch_size, len(samples))
batch_size = min(self.config.optimization.batch_size, len(samples))
offset = (step * batch_size) % len(samples)
batch = samples[offset : offset + batch_size]
if len(batch) < batch_size:
@@ -401,9 +408,11 @@ class DeepCFRTrainer:
) -> float:
last_loss = 0.0
network.train()
for _step in range(max(self.config.advantage_train_steps, 0)):
for _step in range(max(self.config.optimization.advantage_train_steps, 0)):
x, y, legal = self._batch_tensors(
self.advantage_memory.sample(self.config.batch_size, self.rng, player=player)
self.advantage_memory.sample(
self.config.optimization.batch_size, self.rng, player=player
)
)
pred = network(x)
diff = (pred - y).masked_fill(~legal, 0.0)
@@ -422,9 +431,9 @@ class DeepCFRTrainer:
) -> float:
last_loss = 0.0
network.train()
for _step in range(max(self.config.strategy_train_steps, 0)):
for _step in range(max(self.config.optimization.strategy_train_steps, 0)):
x, y, legal = self._batch_tensors(
self.strategy_memory.sample(self.config.batch_size, self.rng)
self.strategy_memory.sample(self.config.optimization.batch_size, self.rng)
)
logits = network(x).masked_fill(~legal, torch.finfo(torch.float32).min)
log_probs = nn.functional.log_softmax(logits, dim=-1).masked_fill(~legal, 0.0)
src/coolrl_lost_cities/games/classic/deep_cfr/workers.py
+23 -22
View File
@@ -40,7 +40,8 @@ def run_traversal_worker_batch(batch: TraversalWorkerBatch) -> TraversalWorkerRe
cfg = config_from_dict(batch.config)
device = torch.device("cpu")
networks = [
DeepCFRMLP(batch.input_dim, batch.action_size, cfg.hidden_size).to(device) for _ in range(2)
DeepCFRMLP(batch.input_dim, batch.action_size, cfg.network.hidden_size).to(device)
for _ in range(2)
]
for network, state_dict in zip(networks, batch.advantage_networks, strict=True):
network.load_state_dict(state_dict)
@@ -48,7 +49,7 @@ def run_traversal_worker_batch(batch: TraversalWorkerBatch) -> TraversalWorkerRe
league_networks: list[list[torch.nn.Module]] = []
for snapshot in batch.league_advantage_networks:
snapshot_networks = [
DeepCFRMLP(batch.input_dim, batch.action_size, cfg.hidden_size).to(device)
DeepCFRMLP(batch.input_dim, batch.action_size, cfg.network.hidden_size).to(device)
for _ in range(2)
]
for network, state_dict in zip(snapshot_networks, snapshot, strict=True):
@@ -63,27 +64,27 @@ def run_traversal_worker_batch(batch: TraversalWorkerBatch) -> TraversalWorkerRe
strategy_memory,
device=device,
action_size=batch.action_size,
epsilon=cfg.regret_matching_epsilon,
strategy_sample_interval=cfg.strategy_sample_interval,
store_strategy_on_traverser_nodes=cfg.store_strategy_on_traverser_nodes,
store_strategy_on_opponent_nodes=cfg.store_strategy_on_opponent_nodes,
max_depth=cfg.max_traversal_depth,
max_nodes=cfg.max_nodes_per_traversal,
outcome_sampling_epsilon=cfg.outcome_sampling_epsilon,
outcome_sampling_value_clip=cfg.outcome_sampling_value_clip,
outcome_unsampled_regret=cfg.outcome_unsampled_regret,
cutoff_value_mode=cfg.cutoff_value_mode,
cutoff_rollouts=cfg.cutoff_rollouts,
cutoff_rollout_policy=cfg.cutoff_rollout_policy,
cutoff_rollout_max_steps=cfg.cutoff_rollout_max_steps,
opponent_policy=cfg.opponent_policy,
epsilon=cfg.traversal.regret_matching_epsilon,
strategy_sample_interval=cfg.traversal.strategy_sample_interval,
store_strategy_on_traverser_nodes=cfg.traversal.store_strategy_on_traverser_nodes,
store_strategy_on_opponent_nodes=cfg.traversal.store_strategy_on_opponent_nodes,
max_depth=cfg.traversal.max_depth,
max_nodes=cfg.traversal.max_nodes,
outcome_sampling_epsilon=cfg.traversal.outcome_sampling_epsilon,
outcome_sampling_value_clip=cfg.traversal.outcome_sampling_value_clip,
outcome_unsampled_regret=cfg.traversal.outcome_unsampled_regret,
cutoff_value_mode=cfg.traversal.cutoff_value_mode,
cutoff_rollouts=cfg.traversal.cutoff_rollouts,
cutoff_rollout_policy=cfg.traversal.cutoff_rollout_policy,
cutoff_rollout_max_steps=cfg.traversal.cutoff_rollout_max_steps,
opponent_policy=cfg.traversal.opponent_policy,
league_advantage_networks=league_networks,
self_play_anchor_probability=cfg.self_play_anchor_probability,
self_play_current_weight=cfg.self_play_current_weight,
self_play_recent_weight=cfg.self_play_recent_weight,
self_play_older_weight=cfg.self_play_older_weight,
self_play_anchor_weight=cfg.self_play_anchor_weight,
self_play_recent_window=cfg.self_play_recent_window,
self_play_anchor_probability=cfg.self_play.anchor_probability,
self_play_current_weight=cfg.self_play.current_weight,
self_play_recent_weight=cfg.self_play.recent_weight,
self_play_older_weight=cfg.self_play.older_weight,
self_play_anchor_weight=cfg.self_play.anchor_weight,
self_play_recent_window=cfg.self_play.recent_window,
rng=np.random.default_rng(batch.worker_seed),
)
game_config = LostCitiesConfig(**batch.game_config)
tests/games/classic/test_deep_cfr_trainer.py
+163 -111
View File
@@ -7,25 +7,43 @@ from coolrl_lost_cities.games.classic.deep_cfr.benchmark import (
benchmark_traversal,
benchmark_traversal_modes,
)
from coolrl_lost_cities.games.classic.deep_cfr.config import DeepCFRConfig
from coolrl_lost_cities.games.classic.deep_cfr.config import DeepCFRConfig, load_config
from coolrl_lost_cities.games.classic.deep_cfr.memory import ReservoirMemory, TrainingSample
from coolrl_lost_cities.games.classic.deep_cfr.trainer import DeepCFRTrainer
from coolrl_lost_cities.games.classic.deep_cfr.traverser import DeepCFRTraverser
def _deep_cfr_config(data: dict) -> DeepCFRConfig:
return DeepCFRConfig.model_validate(data)
def test_deep_cfr_loads_smoke_yaml_config() -> None:
config = load_config("configs/deep_cfr/smoke.yaml")
assert config.run.iterations == 1
assert config.network.hidden_size == 16
assert config.traversal.traversals_per_iteration == 1
assert config.checkpoint.directory == "runs/deep_cfr/smoke"
def test_deep_cfr_trainer_smoke_run() -> None:
trainer = DeepCFRTrainer(
DeepCFRConfig(
iterations=1,
traversals_per_iteration=1,
max_traversal_depth=3,
max_nodes_per_traversal=64,
advantage_train_steps=1,
strategy_train_steps=1,
batch_size=2,
hidden_size=16,
seed=23,
save_every_iteration=False,
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 23},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"max_depth": 3,
"max_nodes": 64,
},
"optimization": {
"advantage_train_steps": 1,
"strategy_train_steps": 1,
"batch_size": 2,
},
"checkpoint": {"save_every_iteration": False},
}
),
LostCitiesConfig(seed=23),
)
@@ -43,15 +61,18 @@ def test_deep_cfr_trainer_smoke_run() -> None:
def test_deep_cfr_recursive_traverser_restores_state_and_collects_samples() -> None:
trainer = DeepCFRTrainer(
DeepCFRConfig(
iterations=1,
traversals_per_iteration=1,
max_traversal_depth=2,
max_nodes_per_traversal=32,
batch_size=2,
hidden_size=16,
seed=29,
save_every_iteration=False,
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 29},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"max_depth": 2,
"max_nodes": 32,
},
"optimization": {"batch_size": 2},
"checkpoint": {"save_every_iteration": False},
}
),
LostCitiesConfig(seed=29),
)
@@ -85,22 +106,25 @@ def test_deep_cfr_recursive_traverser_restores_state_and_collects_samples() -> N
def test_deep_cfr_traverser_supports_outcome_sampling_and_rollout_cutoffs() -> None:
trainer = DeepCFRTrainer(
DeepCFRConfig(
iterations=1,
traversals_per_iteration=1,
max_traversal_depth=1,
max_nodes_per_traversal=32,
outcome_sampling_epsilon=0.25,
outcome_sampling_value_clip=100.0,
outcome_unsampled_regret="zero",
cutoff_value_mode="random_rollout",
cutoff_rollouts=2,
cutoff_rollout_policy="random",
cutoff_rollout_max_steps=16,
batch_size=2,
hidden_size=16,
seed=31,
save_every_iteration=False,
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 31},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"max_depth": 1,
"max_nodes": 32,
"outcome_sampling_epsilon": 0.25,
"outcome_sampling_value_clip": 100.0,
"outcome_unsampled_regret": "zero",
"cutoff_value_mode": "random_rollout",
"cutoff_rollouts": 2,
"cutoff_rollout_policy": "random",
"cutoff_rollout_max_steps": 16,
},
"optimization": {"batch_size": 2},
"checkpoint": {"save_every_iteration": False},
}
),
LostCitiesConfig(seed=31),
)
@@ -161,19 +185,22 @@ def test_reservoir_memory_caps_samples_and_filters_player_batches() -> None:
def test_deep_cfr_trainer_saves_loads_and_evaluates_checkpoint(tmp_path) -> None:
checkpoint_dir = tmp_path / "deep_cfr"
trainer = DeepCFRTrainer(
DeepCFRConfig(
iterations=1,
traversals_per_iteration=1,
max_traversal_depth=2,
max_nodes_per_traversal=32,
batch_size=2,
hidden_size=16,
seed=41,
checkpoint_dir=str(checkpoint_dir),
save_every_iteration=True,
eval_every=1,
eval_games=2,
eval_opponents=("random",),
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 41},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"max_depth": 2,
"max_nodes": 32,
},
"optimization": {"batch_size": 2},
"checkpoint": {
"directory": str(checkpoint_dir),
"save_every_iteration": True,
},
"evaluation": {"eval_every": 1, "games": 2, "opponents": ("random",)},
}
),
LostCitiesConfig(seed=41),
)
@@ -181,11 +208,15 @@ def test_deep_cfr_trainer_saves_loads_and_evaluates_checkpoint(tmp_path) -> None
metrics = trainer.train()
latest = checkpoint_dir / "latest.pt"
restored = DeepCFRTrainer(
DeepCFRConfig(
hidden_size=16,
seed=41,
checkpoint_dir=str(checkpoint_dir),
save_every_iteration=False,
_deep_cfr_config(
{
"run": {"seed": 41},
"network": {"hidden_size": 16},
"checkpoint": {
"directory": str(checkpoint_dir),
"save_every_iteration": False,
},
}
),
LostCitiesConfig(seed=41),
)
@@ -202,18 +233,23 @@ def test_deep_cfr_trainer_saves_loads_and_evaluates_checkpoint(tmp_path) -> None
def test_deep_cfr_trainer_multiprocessing_smoke_run(tmp_path) -> None:
trainer = DeepCFRTrainer(
DeepCFRConfig(
iterations=1,
traversals_per_iteration=2,
max_traversal_depth=2,
max_nodes_per_traversal=32,
batch_size=2,
hidden_size=16,
seed=43,
checkpoint_dir=str(tmp_path / "mp"),
save_every_iteration=False,
num_workers=2,
traversal_worker_chunk_size=1,
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 43},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 2,
"max_depth": 2,
"max_nodes": 32,
"num_workers": 2,
"worker_chunk_size": 1,
},
"optimization": {"batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "mp"),
"save_every_iteration": False,
},
}
),
LostCitiesConfig(seed=43),
)
@@ -226,24 +262,26 @@ def test_deep_cfr_trainer_multiprocessing_smoke_run(tmp_path) -> None:
def test_deep_cfr_traversal_benchmark_smoke() -> None:
result = benchmark_traversal(
DeepCFRConfig(
traversals_per_iteration=1,
max_traversal_depth=2,
hidden_size=16,
save_every_iteration=False,
seed=47,
_deep_cfr_config(
{
"run": {"seed": 47},
"network": {"hidden_size": 16},
"traversal": {"traversals_per_iteration": 1, "max_depth": 2},
"checkpoint": {"save_every_iteration": False},
}
)
)
assert result["traversal_nodes"] > 0
assert result["nodes_per_second"] > 0.0
comparison = benchmark_traversal_modes(
DeepCFRConfig(
traversals_per_iteration=1,
max_traversal_depth=2,
hidden_size=16,
save_every_iteration=False,
seed=48,
_deep_cfr_config(
{
"run": {"seed": 48},
"network": {"hidden_size": 16},
"traversal": {"traversals_per_iteration": 1, "max_depth": 2},
"checkpoint": {"save_every_iteration": False},
}
)
)
assert comparison["summary"]["speedup"] > 0.0
@@ -251,20 +289,27 @@ def test_deep_cfr_traversal_benchmark_smoke() -> None:
def test_deep_cfr_self_play_league_records_snapshots(tmp_path) -> None:
trainer = DeepCFRTrainer(
DeepCFRConfig(
iterations=2,
traversals_per_iteration=1,
max_traversal_depth=2,
max_nodes_per_traversal=32,
batch_size=2,
hidden_size=16,
seed=53,
checkpoint_dir=str(tmp_path / "league"),
save_every_iteration=False,
opponent_policy="self_play_league",
self_play_snapshot_every=1,
self_play_max_snapshots=1,
self_play_anchor_probability=1.0,
_deep_cfr_config(
{
"run": {"iterations": 2, "seed": 53},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"max_depth": 2,
"max_nodes": 32,
"opponent_policy": "self_play_league",
},
"self_play": {
"snapshot_every": 1,
"max_snapshots": 1,
"anchor_probability": 1.0,
},
"optimization": {"batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "league"),
"save_every_iteration": False,
},
}
),
LostCitiesConfig(seed=53),
)
@@ -277,24 +322,31 @@ def test_deep_cfr_self_play_league_records_snapshots(tmp_path) -> None:
def test_deep_cfr_weighted_self_play_league_uses_snapshot_bucket(tmp_path) -> None:
trainer = DeepCFRTrainer(
DeepCFRConfig(
iterations=2,
traversals_per_iteration=1,
max_traversal_depth=2,
max_nodes_per_traversal=32,
batch_size=2,
hidden_size=16,
seed=59,
checkpoint_dir=str(tmp_path / "weighted-league"),
save_every_iteration=False,
opponent_policy="self_play_league",
self_play_snapshot_every=1,
self_play_max_snapshots=2,
self_play_current_weight=0.0,
self_play_recent_weight=1.0,
self_play_older_weight=0.0,
self_play_anchor_weight=0.0,
self_play_recent_window=1,
_deep_cfr_config(
{
"run": {"iterations": 2, "seed": 59},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"max_depth": 2,
"max_nodes": 32,
"opponent_policy": "self_play_league",
},
"self_play": {
"snapshot_every": 1,
"max_snapshots": 2,
"current_weight": 0.0,
"recent_weight": 1.0,
"older_weight": 0.0,
"anchor_weight": 0.0,
"recent_window": 1,
},
"optimization": {"batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "weighted-league"),
"save_every_iteration": False,
},
}
),
LostCitiesConfig(seed=59),
)
uv.lock
Generated
+142
View File
@@ -2,6 +2,15 @@ version = 1
revision = 3
requires-python = ">=3.11"
[[package]]
name = "annotated-types"
version = "0.7.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
]
[[package]]
name = "cfgv"
version = "3.5.0"
@@ -26,6 +35,8 @@ version = "0.1.0"
source = { editable = "." }
dependencies = [
{ name = "numpy" },
{ name = "pydantic" },
{ name = "pyyaml" },
{ name = "torch" },
]
@@ -46,8 +57,10 @@ dev = [
[package.metadata]
requires-dist = [
{ name = "numpy", specifier = ">=1.26.0" },
{ name = "pydantic", specifier = ">=2.7" },
{ name = "pygame", marker = "extra == 'gui'", specifier = ">=2.6.1" },
{ name = "pygame-gui", marker = "extra == 'gui'", specifier = ">=0.6.14" },
{ name = "pyyaml", specifier = ">=6.0" },
{ name = "torch", specifier = ">=2.3.0" },
]
provides-extras = ["gui"]
@@ -594,6 +607,123 @@ wheels = [
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]
[[package]]
name = "pydantic"
version = "2.13.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "annotated-types" },
{ name = "pydantic-core" },
{ name = "typing-extensions" },
{ name = "typing-inspection" },
]
sdist = { url = "https://files.pythonhosted.org/packages/18/a5/b60d21ac674192f8ab0ba4e9fd860690f9b4a6e51ca5df118733b487d8d6/pydantic-2.13.4.tar.gz", hash = "sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6", size = 844775, upload-time = "2026-05-06T13:43:05.343Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/fd/7b/122376b1fd3c62c1ed9dc80c931ace4844b3c55407b6fb2d199377c9736f/pydantic-2.13.4-py3-none-any.whl", hash = "sha256:45a282cde31d808236fd7ea9d919b128653c8b38b393d1c4ab335c62924d9aba", size = 472262, upload-time = "2026-05-06T13:43:02.641Z" },
]
[[package]]
name = "pydantic-core"
version = "2.46.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "typing-extensions" },
]
sdist = { url = "https://files.pythonhosted.org/packages/9d/56/921726b776ace8d8f5db44c4ef961006580d91dc52b803c489fafd1aa249/pydantic_core-2.46.4.tar.gz", hash = "sha256:62f875393d7f270851f20523dd2e29f082bcc82292d66db2b64ea71f64b6e1c1", size = 471464, upload-time = "2026-05-06T13:37:06.98Z" }
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name = "virtualenv"
version = "21.3.1"