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commander-ai-lab/ml/training/ppo_trainer.py
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rgriffithandClaude Opus 4.6 9b79d18201 feat(lan-client): triggers panel + CMD DMG score badges + fix encoding project-wide 
Left action panel revised to show triggers list + Create Token/Auto-Arrange.
CMD DMG badges added to center life-panel score areas (click opens matrix).
Removed renderEnhancedPanels calls that split the battlefield on damage adjust.
Added encoding='utf-8' to all bare open() calls across 17 Python files to
prevent cp1252 crashes on MTG card text with smart quotes/em-dashes.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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"""
Commander AI Lab — PPO Trainer
═══════════════════════════════
Proximal Policy Optimization (Clip) with:
- Clipped surrogate objective for policy
- Value function loss (clipped)
- Entropy bonus for exploration
- Generalized Advantage Estimation (computed in RolloutBuffer)
- Self-play episode collection between updates
- Periodic checkpointing and evaluation
- Win-rate tracking against opponent policies
- Forge-based episode source via --data-source forge|mixed
Reference: Schulman et al., "Proximal Policy Optimization Algorithms", 2017
Usage:
python -m ml.training.ppo_trainer --iterations 100 --episodes-per-iter 64
python -m ml.training.ppo_trainer --load-supervised ml/models/checkpoints/best_policy.pt
python -m ml.training.ppo_trainer --data-source forge --forge-results-dir results --iterations 50
python -m ml.training.ppo_trainer --data-source mixed --forge-weight 0.7 --iterations 100
"""
import argparse
import asyncio
import json
import logging
import os
import sys
import threading
import time
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
project_root = str(Path(__file__).parent.parent.parent)
if project_root not in sys.path:
sys.path.insert(0, project_root)
# Conditional torch import
try:
import torch
import torch.nn as nn
import torch.nn.functional as F
TORCH_AVAILABLE = True
except ImportError:
TORCH_AVAILABLE = False
from ml.config.scope import NUM_ACTIONS, STATE_DIMS, TRAINING_CONFIG
from ml.training.rollout_buffer import RolloutBuffer
from ml.training.reward import RewardConfig
from ml.training.self_play import (
collect_rollouts, HeuristicPolicy, RandomPolicy,
)
logger = logging.getLogger("ml.ppo")
# ══════════════════════════════════════════════════════════
# Forge data-source integration
# ══════════════════════════════════════════════════════════
def episodes_to_buffer(episodes, buffer: RolloutBuffer) -> int:
"""Drain a list of ForgeEpisodes into an existing RolloutBuffer.
Returns the number of transitions added.
"""
added = 0
for ep in episodes:
for t in ep.transitions:
if buffer.size >= buffer.buffer_size:
break
buffer.add(
state=t.state,
action=t.action,
reward=t.reward,
log_prob=t.log_prob,
value=t.value,
done=t.done,
)
added += 1
return added
class ForgeDataSource:
"""Runs the Forge producer pipeline in a background thread and
exposes a synchronous drain() method for the PPO training loop.
Usage::
source = ForgeDataSource(results_dir="results", batch_id="run1", num_games=64)
source.start()
# ... in training loop ...
episodes = source.drain()
source.stop()
"""
def __init__(
self,
results_dir: str = "results",
batch_id: str = "forge-batch",
num_games: int = 256,
reward_config: Optional[RewardConfig] = None,
queue_size: int = 256,
):
self.results_dir = results_dir
self.batch_id = batch_id
self.num_games = num_games
self.reward_config = reward_config or RewardConfig()
self.queue_size = queue_size
self._episodes: List = []
self._lock = threading.Lock()
self._thread: Optional[threading.Thread] = None
self._stop_event = threading.Event()
def start(self):
"""Launch the background producer thread."""
self._thread = threading.Thread(target=self._run, daemon=True)
self._thread.start()
logger.info("[ForgeDataSource] Producer started (results_dir=%s, batch_id=%s, num_games=%d)",
self.results_dir, self.batch_id, self.num_games)
def _run(self):
from ml.training.forge_episode_generator import ForgeEpisodeQueue, forge_producer
async def _produce():
queue = ForgeEpisodeQueue(max_size=self.queue_size)
produced = await forge_producer(
queue=queue,
results_dir=self.results_dir,
batch_id=self.batch_id,
num_games=self.num_games,
reward_config=self.reward_config,
)
queue.close()
# Drain remaining items
while not queue.is_closed:
ep = await queue.get(timeout=1.0)
if ep is not None:
with self._lock:
self._episodes.append(ep)
logger.info("[ForgeDataSource] Producer finished: %d episodes", produced)
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
loop.run_until_complete(_produce())
finally:
loop.close()
def drain(self) -> List:
"""Return and clear all buffered episodes (thread-safe)."""
with self._lock:
eps = list(self._episodes)
self._episodes.clear()
return eps
def stop(self):
self._stop_event.set()
if self._thread:
self._thread.join(timeout=5.0)
# ══════════════════════════════════════════════════════════
# PPO Config
# ══════════════════════════════════════════════════════════
class PPOConfig:
"""PPO hyperparameters."""
def __init__(
self,
# Training loop
iterations: int = 100,
episodes_per_iter: int = 64,
ppo_epochs: int = 4,
batch_size: int = 256,
# PPO core
clip_epsilon: float = 0.2,
value_clip: float = 0.2,
entropy_coeff: float = 0.01,
value_loss_coeff: float = 0.5,
max_grad_norm: float = 0.5,
# Optimizer
learning_rate: float = 3e-4,
lr_schedule: str = "constant", # constant | linear | cosine
min_lr: float = 1e-5,
# Rollout
buffer_size: int = 4096,
gamma: float = 0.99,
gae_lambda: float = 0.95,
# Opponent
opponent: str = "heuristic", # heuristic | random | self
playstyle: str = "midrange",
# Checkpointing
checkpoint_dir: str = "ml/models/checkpoints",
save_every: int = 10,
eval_every: int = 5,
eval_episodes: int = 50,
# Supervised preload
load_supervised: Optional[str] = None,
# Data source
data_source: str = "synthetic", # synthetic | forge | mixed
forge_results_dir: str = "results",
forge_batch_id: str = "forge-batch",
forge_num_games: int = 256,
forge_weight: float = 0.7, # fraction of buffer from Forge when mixed
):
self.iterations = iterations
self.episodes_per_iter = episodes_per_iter
self.ppo_epochs = ppo_epochs
self.batch_size = batch_size
self.clip_epsilon = clip_epsilon
self.value_clip = value_clip
self.entropy_coeff = entropy_coeff
self.value_loss_coeff = value_loss_coeff
self.max_grad_norm = max_grad_norm
self.learning_rate = learning_rate
self.lr_schedule = lr_schedule
self.min_lr = min_lr
self.buffer_size = buffer_size
self.gamma = gamma
self.gae_lambda = gae_lambda
self.opponent = opponent
self.playstyle = playstyle
self.checkpoint_dir = checkpoint_dir
self.save_every = save_every
self.eval_every = eval_every
self.eval_episodes = eval_episodes
self.load_supervised = load_supervised
self.data_source = data_source
self.forge_results_dir = forge_results_dir
self.forge_batch_id = forge_batch_id
self.forge_num_games = forge_num_games
self.forge_weight = max(0.0, min(1.0, forge_weight))
# ══════════════════════════════════════════════════════════
# PPO Trainer
# ══════════════════════════════════════════════════════════
class PPOTrainer:
"""Proximal Policy Optimization trainer with self-play and optional Forge data source."""
def __init__(self, config: PPOConfig):
if not TORCH_AVAILABLE:
raise ImportError("PyTorch required for PPO training. Install: pip install torch")
self.config = config
self.device = self._detect_device()
from ml.training.policy_network import PolicyValueNetwork
self.model = PolicyValueNetwork(
input_dim=STATE_DIMS.total_state_dim,
hidden_dim=TRAINING_CONFIG.hidden_dim,
num_actions=NUM_ACTIONS,
num_layers=TRAINING_CONFIG.num_layers,
dropout=0.0,
).to(self.device)
if config.load_supervised and os.path.exists(config.load_supervised):
self._load_supervised_weights(config.load_supervised)
self.optimizer = torch.optim.Adam(
self.model.parameters(),
lr=config.learning_rate,
eps=1e-5,
)
self.scheduler = None
if config.lr_schedule == "linear":
self.scheduler = torch.optim.lr_scheduler.LinearLR(
self.optimizer,
start_factor=1.0,
end_factor=config.min_lr / config.learning_rate,
total_iters=config.iterations,
)
elif config.lr_schedule == "cosine":
self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
self.optimizer,
T_max=config.iterations,
eta_min=config.min_lr,
)
self.buffer = RolloutBuffer(
buffer_size=config.buffer_size,
state_dim=STATE_DIMS.total_state_dim,
gamma=config.gamma,
gae_lambda=config.gae_lambda,
)
self.reward_config = RewardConfig(
gamma=config.gamma,
gae_lambda=config.gae_lambda,
)
self.opponent = self._create_opponent(config.opponent)
# Forge data source (started lazily in train())
self._forge_source: Optional[ForgeDataSource] = None
if config.data_source in ("forge", "mixed"):
self._forge_source = ForgeDataSource(
results_dir=config.forge_results_dir,
batch_id=config.forge_batch_id,
num_games=config.forge_num_games,
reward_config=self.reward_config,
)
self.iteration = 0
self.total_steps = 0
self.best_win_rate = 0.0
self.history = []
os.makedirs(config.checkpoint_dir, exist_ok=True)
def _detect_device(self) -> str:
if torch.cuda.is_available():
return "cuda"
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
return "mps"
return "cpu"
def _load_supervised_weights(self, path: str):
logger.info("Loading supervised weights from %s", path)
checkpoint = torch.load(path, map_location=self.device, weights_only=False)
state_dict = checkpoint.get("model_state_dict", checkpoint)
model_dict = self.model.state_dict()
compatible = {k: v for k, v in state_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
model_dict.update(compatible)
self.model.load_state_dict(model_dict)
logger.info("Loaded %d/%d parameter tensors from supervised checkpoint",
len(compatible), len(state_dict))
def _create_opponent(self, opponent_type: str):
if opponent_type == "random":
return RandomPolicy()
elif opponent_type == "heuristic":
return HeuristicPolicy()
elif opponent_type == "self":
return self._create_self_play_opponent()
return HeuristicPolicy()
def _create_self_play_opponent(self):
class SelfPlayPolicy:
def __init__(self, model, device):
self.model = model
self.device = device
def select_action(self, state_vec):
state_tensor = torch.from_numpy(state_vec).unsqueeze(0).to(self.device)
with torch.no_grad():
logits, _ = self.model.forward_with_value(state_tensor)
probs = F.softmax(logits, dim=-1)
dist = torch.distributions.Categorical(probs)
action = dist.sample()
log_prob = dist.log_prob(action)
return action.item(), log_prob.item()
return SelfPlayPolicy(self.model, self.device)
def _collect_forge_rollouts(self) -> Dict:
"""Drain buffered Forge episodes into the RolloutBuffer.
Returns stats dict compatible with collect_rollouts() output.
"""
episodes = self._forge_source.drain()
if not episodes:
logger.warning("[Forge] No episodes available yet — waiting for JSONL output in %s",
self.config.forge_results_dir)
return {"steps": 0, "win_rate": 0.0, "avg_reward_per_ep": 0.0,
"wins": 0, "losses": 0, "draws": 0, "source": "forge"}
added = episodes_to_buffer(episodes, self.buffer)
wins = sum(1 for ep in episodes if ep.winner == 0)
win_rate = wins / max(len(episodes), 1)
avg_reward = sum(ep.total_reward for ep in episodes) / max(len(episodes), 1)
logger.info("[Forge] Drained %d episodes (%d transitions) into buffer — WR=%.0f%% AvgR=%.3f",
len(episodes), added, win_rate * 100, avg_reward)
return {
"steps": added,
"win_rate": win_rate,
"avg_reward_per_ep": avg_reward,
"wins": wins,
"losses": len(episodes) - wins,
"draws": 0,
"source": "forge",
}
def _collect_rollouts_for_iter(self) -> Dict:
"""Collect rollouts according to configured data_source.
- synthetic: pure self_play.collect_rollouts (original behaviour)
- forge: pure Forge episode drain
- mixed: Forge fills forge_weight fraction of buffer, synthetic fills remainder
"""
cfg = self.config
self.buffer.reset()
self.model.eval()
if cfg.data_source == "forge":
stats = self._collect_forge_rollouts()
elif cfg.data_source == "mixed":
forge_cap = int(cfg.buffer_size * cfg.forge_weight)
synth_cap = cfg.buffer_size - forge_cap
# Temporarily shrink buffer for Forge slice
original_size = self.buffer.buffer_size
self.buffer.buffer_size = forge_cap
forge_stats = self._collect_forge_rollouts()
self.buffer.buffer_size = original_size
synth_eps = max(1, int(cfg.episodes_per_iter * (1.0 - cfg.forge_weight)))
synth_stats = collect_rollouts(
agent_model=self.model,
opponent_policy=self.opponent,
buffer=self.buffer,
num_episodes=synth_eps,
playstyle=cfg.playstyle,
reward_config=self.reward_config,
)
stats = {
"steps": forge_stats["steps"] + synth_stats["steps"],
"win_rate": (
forge_stats["win_rate"] * cfg.forge_weight
+ synth_stats["win_rate"] * (1.0 - cfg.forge_weight)
),
"avg_reward_per_ep": (
forge_stats["avg_reward_per_ep"] * cfg.forge_weight
+ synth_stats["avg_reward_per_ep"] * (1.0 - cfg.forge_weight)
),
"wins": forge_stats["wins"] + synth_stats["wins"],
"losses": forge_stats["losses"] + synth_stats["losses"],
"draws": synth_stats.get("draws", 0),
"source": "mixed",
}
else: # synthetic (default — unchanged behaviour)
stats = collect_rollouts(
agent_model=self.model,
opponent_policy=self.opponent,
buffer=self.buffer,
num_episodes=cfg.episodes_per_iter,
playstyle=cfg.playstyle,
reward_config=self.reward_config,
)
stats["source"] = "synthetic"
self.model.train()
return stats
def ppo_update(self) -> Dict:
"""Run PPO update epochs on the collected rollout buffer."""
cfg = self.config
total_policy_loss = 0.0
total_value_loss = 0.0
total_entropy = 0.0
total_approx_kl = 0.0
total_clip_frac = 0.0
num_updates = 0
for epoch in range(cfg.ppo_epochs):
for batch in self.buffer.get_batches(cfg.batch_size):
states, actions, old_log_probs, advantages, returns = batch
states_t = torch.from_numpy(states).to(self.device)
actions_t = torch.from_numpy(actions).to(self.device)
old_log_probs_t = torch.from_numpy(old_log_probs).to(self.device)
advantages_t = torch.from_numpy(advantages).to(self.device)
returns_t = torch.from_numpy(returns).to(self.device)
logits, values = self.model.forward_with_value(states_t)
values = values.squeeze(-1)
probs = F.softmax(logits, dim=-1)
dist = torch.distributions.Categorical(probs)
new_log_probs = dist.log_prob(actions_t)
entropy = dist.entropy().mean()
ratio = torch.exp(new_log_probs - old_log_probs_t)
surr1 = ratio * advantages_t
surr2 = torch.clamp(ratio, 1.0 - cfg.clip_epsilon, 1.0 + cfg.clip_epsilon) * advantages_t
policy_loss = -torch.min(surr1, surr2).mean()
if cfg.value_clip > 0:
value_loss_unclipped = (values - returns_t) ** 2
value_loss = 0.5 * value_loss_unclipped.mean()
else:
value_loss = 0.5 * ((values - returns_t) ** 2).mean()
loss = (
policy_loss
+ cfg.value_loss_coeff * value_loss
- cfg.entropy_coeff * entropy
)
self.optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), cfg.max_grad_norm)
self.optimizer.step()
with torch.no_grad():
approx_kl = ((ratio - 1) - torch.log(ratio)).mean().item()
clip_frac = ((ratio - 1.0).abs() > cfg.clip_epsilon).float().mean().item()
total_policy_loss += policy_loss.item()
total_value_loss += value_loss.item()
total_entropy += entropy.item()
total_approx_kl += approx_kl
total_clip_frac += clip_frac
num_updates += 1
n = max(num_updates, 1)
return {
"policy_loss": total_policy_loss / n,
"value_loss": total_value_loss / n,
"entropy": total_entropy / n,
"approx_kl": total_approx_kl / n,
"clip_fraction": total_clip_frac / n,
"num_updates": num_updates,
}
def evaluate(self, num_episodes: int = 50) -> Dict:
"""Evaluate the current policy against heuristic and random opponents."""
self.model.eval()
results = {}
for opp_name, opp_policy in [
("heuristic", HeuristicPolicy()),
("random", RandomPolicy()),
]:
eval_buffer = RolloutBuffer(
buffer_size=num_episodes * 50,
state_dim=STATE_DIMS.total_state_dim,
gamma=self.config.gamma,
gae_lambda=self.config.gae_lambda,
)
stats = collect_rollouts(
agent_model=self.model,
opponent_policy=opp_policy,
buffer=eval_buffer,
num_episodes=num_episodes,
playstyle=self.config.playstyle,
reward_config=self.reward_config,
)
results[opp_name] = {
"win_rate": stats["win_rate"],
"wins": stats["wins"],
"losses": stats["losses"],
"draws": stats["draws"],
"avg_reward": stats["avg_reward_per_ep"],
"avg_steps": stats["avg_steps_per_ep"],
}
self.model.train()
return results
def save_checkpoint(self, path: str = None, metrics: dict = None):
if path is None:
path = os.path.join(
self.config.checkpoint_dir,
f"ppo_iter_{self.iteration:04d}.pt",
)
os.makedirs(os.path.dirname(path), exist_ok=True)
torch.save({
"iteration": self.iteration,
"total_steps": self.total_steps,
"model_state_dict": self.model.state_dict(),
"optimizer_state_dict": self.optimizer.state_dict(),
"metrics": metrics or {},
"config": {
"clip_epsilon": self.config.clip_epsilon,
"entropy_coeff": self.config.entropy_coeff,
"learning_rate": self.config.learning_rate,
"opponent": self.config.opponent,
"playstyle": self.config.playstyle,
"data_source": self.config.data_source,
},
"best_win_rate": self.best_win_rate,
"model_config": {
"input_dim": STATE_DIMS.total_state_dim,
"hidden_dim": TRAINING_CONFIG.hidden_dim,
"num_actions": NUM_ACTIONS,
"num_layers": TRAINING_CONFIG.num_layers,
"dropout": 0.0,
},
}, path)
logger.info("Checkpoint saved: %s", path)
def train(self, progress_callback=None) -> Dict:
"""Run the full PPO training loop."""
cfg = self.config
logger.info("=" * 60)
logger.info("PPO Training — Commander AI Lab")
logger.info("=" * 60)
logger.info(" Iterations: %d", cfg.iterations)
logger.info(" Episodes/iter: %d", cfg.episodes_per_iter)
logger.info(" PPO epochs: %d", cfg.ppo_epochs)
logger.info(" Batch size: %d", cfg.batch_size)
logger.info(" Clip epsilon: %.2f", cfg.clip_epsilon)
logger.info(" Entropy coeff: %.4f", cfg.entropy_coeff)
logger.info(" Learning rate: %.6f", cfg.learning_rate)
logger.info(" Opponent: %s", cfg.opponent)
logger.info(" Device: %s", self.device)
logger.info(" Supervised: %s", cfg.load_supervised or "None")
logger.info(" Data source: %s", cfg.data_source)
if cfg.data_source in ("forge", "mixed"):
logger.info(" Forge results: %s / %s", cfg.forge_results_dir, cfg.forge_batch_id)
logger.info(" Forge weight: %.0f%%", cfg.forge_weight * 100)
logger.info("")
# Start Forge producer background thread if needed
if self._forge_source:
self._forge_source.start()
t_start = time.time()
try:
for iteration in range(1, cfg.iterations + 1):
self.iteration = iteration
t_iter = time.time()
rollout_stats = self._collect_rollouts_for_iter()
self.total_steps += rollout_stats["steps"]
update_metrics = self.ppo_update()
current_lr = self.optimizer.param_groups[0]["lr"]
if self.scheduler:
self.scheduler.step()
eval_results = None
if iteration % cfg.eval_every == 0 or iteration == 1:
eval_results = self.evaluate(cfg.eval_episodes)
heuristic_wr = eval_results.get("heuristic", {}).get("win_rate", 0)
if heuristic_wr > self.best_win_rate:
self.best_win_rate = heuristic_wr
best_path = os.path.join(cfg.checkpoint_dir, "best_ppo.pt")
self.save_checkpoint(best_path, {"eval": eval_results})
logger.info(" ★ New best win rate: %.1f%%", heuristic_wr * 100)
if iteration % cfg.save_every == 0:
self.save_checkpoint()
iter_time = time.time() - t_iter
metrics = {
"iteration": iteration,
"steps": rollout_stats["steps"],
"total_steps": self.total_steps,
"win_rate": rollout_stats["win_rate"],
"avg_reward": rollout_stats["avg_reward_per_ep"],
"policy_loss": update_metrics["policy_loss"],
"value_loss": update_metrics["value_loss"],
"entropy": update_metrics["entropy"],
"approx_kl": update_metrics["approx_kl"],
"clip_fraction": update_metrics["clip_fraction"],
"lr": current_lr,
"iter_time_s": round(iter_time, 1),
"data_source": rollout_stats.get("source", cfg.data_source),
"eval": eval_results,
}
self.history.append(metrics)
eval_str = ""
if eval_results:
h_wr = eval_results.get("heuristic", {}).get("win_rate", 0)
r_wr = eval_results.get("random", {}).get("win_rate", 0)
eval_str = f" | eval: h={h_wr:.0%} r={r_wr:.0%}"
src_tag = f"[{rollout_stats.get('source', '?')[:3]}]"
logger.info(
" [%3d/%d]%s wr=%.0f%% rew=%.3f ploss=%.4f vloss=%.4f ent=%.3f kl=%.4f%s (%.1fs)",
iteration, cfg.iterations, src_tag,
rollout_stats["win_rate"] * 100,
rollout_stats["avg_reward_per_ep"],
update_metrics["policy_loss"],
update_metrics["value_loss"],
update_metrics["entropy"],
update_metrics["approx_kl"],
eval_str,
iter_time,
)
if progress_callback:
progress_callback(iteration, metrics)
finally:
if self._forge_source:
self._forge_source.stop()
total_time = time.time() - t_start
self.save_checkpoint()
history_path = os.path.join(cfg.checkpoint_dir, "ppo_history.json")
with open(history_path, "w", encoding="utf-8") as f:
json.dump(self.history, f, indent=2, default=str)
summary = {
"iterations": cfg.iterations,
"total_steps": self.total_steps,
"best_win_rate": self.best_win_rate,
"final_win_rate": self.history[-1]["win_rate"] if self.history else 0,
"total_time_s": round(total_time, 1),
"device": self.device,
"data_source": cfg.data_source,
"checkpoint_dir": cfg.checkpoint_dir,
"history_path": history_path,
}
logger.info("")
logger.info("=" * 60)
logger.info("PPO Training Complete")
logger.info(" Total time: %.1f s", total_time)
logger.info(" Total steps: %d", self.total_steps)
logger.info(" Best win rate: %.1f%%", self.best_win_rate * 100)
logger.info(" Data source: %s", cfg.data_source)
logger.info(" History: %s", history_path)
logger.info("=" * 60)
return summary
def main():
"""CLI entry point for PPO training."""
parser = argparse.ArgumentParser(description="Commander AI Lab — PPO Training")
# Training loop
parser.add_argument("--iterations", type=int, default=100)
parser.add_argument("--episodes-per-iter", type=int, default=64)
parser.add_argument("--ppo-epochs", type=int, default=4)
parser.add_argument("--batch-size", type=int, default=256)
# PPO hyperparams
parser.add_argument("--clip-epsilon", type=float, default=0.2)
parser.add_argument("--entropy-coeff", type=float, default=0.01)
parser.add_argument("--value-loss-coeff", type=float, default=0.5)
parser.add_argument("--lr", type=float, default=3e-4)
parser.add_argument("--lr-schedule", default="constant", choices=["constant", "linear", "cosine"])
# Rollout
parser.add_argument("--buffer-size", type=int, default=4096)
parser.add_argument("--gamma", type=float, default=0.99)
parser.add_argument("--gae-lambda", type=float, default=0.95)
# Opponent
parser.add_argument("--opponent", default="heuristic", choices=["heuristic", "random", "self"])
parser.add_argument("--playstyle", default="midrange")
# Checkpointing
parser.add_argument("--checkpoint-dir", default="ml/models/checkpoints")
parser.add_argument("--save-every", type=int, default=10)
parser.add_argument("--eval-every", type=int, default=5)
parser.add_argument("--eval-episodes", type=int, default=50)
# Init
parser.add_argument("--load-supervised", default=None,
help="Path to supervised checkpoint to initialize from")
# Data source
parser.add_argument(
"--data-source", default="synthetic",
choices=["synthetic", "forge", "mixed"],
help="Episode source: synthetic (self-play), forge (Forge JSONL), mixed (blend)",
)
parser.add_argument(
"--forge-results-dir", default="results",
help="Directory where Forge writes ml-decisions-forge-*.jsonl files",
)
parser.add_argument(
"--forge-batch-id", default="forge-batch",
help="Batch ID prefix for Forge JSONL files (matches BatchRunner --batchId)",
)
parser.add_argument(
"--forge-num-games", type=int, default=256,
help="Total Forge games to consume before stopping the producer",
)
parser.add_argument(
"--forge-weight", type=float, default=0.7,
help="Fraction of RolloutBuffer filled from Forge when --data-source mixed (0.0–1.0)",
)
args = parser.parse_args()
logging.basicConfig(level=logging.INFO, format="%(message)s")
config = PPOConfig(
iterations=args.iterations,
episodes_per_iter=args.episodes_per_iter,
ppo_epochs=args.ppo_epochs,
batch_size=args.batch_size,
clip_epsilon=args.clip_epsilon,
entropy_coeff=args.entropy_coeff,
value_loss_coeff=args.value_loss_coeff,
learning_rate=args.lr,
lr_schedule=args.lr_schedule,
buffer_size=args.buffer_size,
gamma=args.gamma,
gae_lambda=args.gae_lambda,
opponent=args.opponent,
playstyle=args.playstyle,
checkpoint_dir=args.checkpoint_dir,
save_every=args.save_every,
eval_every=args.eval_every,
eval_episodes=args.eval_episodes,
load_supervised=args.load_supervised,
data_source=args.data_source,
forge_results_dir=args.forge_results_dir,
forge_batch_id=args.forge_batch_id,
forge_num_games=args.forge_num_games,
forge_weight=args.forge_weight,
)
trainer = PPOTrainer(config)
summary = trainer.train()
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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