Files
commander-ai-lab/routes/policy.py
T
KoalaTrapLord 411fa41063 feat(83): Phase 2 — Live Forge-to-Policy-Server IPC (#100)
* feat(83): add PolicyClient for live Forge-to-policy-server IPC (#83)

Phase 2.1: Bridge between Forge decision points and Python policy
server for online training. Calls /api/policy/decide endpoint,
collects (state, action, reward) tuples in real-time, and flushes
them to the server for online PPO updates.

Falls back to Forge built-in AI if server is unavailable.

* feat(83): add /api/policy/* routes for live Forge IPC (#83)

Phase 2.2: Policy decision routes for real-time Forge IPC.

- POST /api/policy/decide — full game state inference with log_prob/value
- POST /api/policy/collect — online learning tuple batch collection
- POST /api/policy/reward — end-of-game reward propagation
- GET /api/policy/health — readiness check for Java PolicyClient
- GET /api/policy/stats — session statistics

Includes Forge phase mapping and PlayerZoneState with full zone contents.

* feat(83): integrate PolicyClient into GameSession for bidirectional sync (#83)
2026-03-23 22:08:37 -04:00

322 lines
9.9 KiB
Python

"""Commander AI Lab — Policy Decision Routes (Phase 2)
════════════════════════════════════════════════════
Live Forge ↔ policy server IPC endpoints for online training.
These endpoints are purpose-built for the real-time decision loop
where Forge's Java side calls the Python policy server at each
game decision point.
Endpoints:
POST /api/policy/decide — Accept full Forge game state, return action + log_prob + value
POST /api/policy/collect — Accept batch of (state, action, reward) tuples
POST /api/policy/reward — Submit end-of-game reward
GET /api/policy/health — Readiness check for the live IPC loop
GET /api/policy/stats — Online learning session statistics
Refs: Issue #83 Phase 2.2
"""
import logging
import time
from typing import Dict, List, Optional
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
logger = logging.getLogger("routes.policy")
router = APIRouter(prefix="/api/policy", tags=["policy"])
# ================================================================
# Request / Response Models
# ================================================================
class PlayerZoneState(BaseModel):
"""Per-player zone state from Forge."""
seat: int = 0
name: str = ""
life: int = 40
poison: int = 0
cmdr_dmg: Dict[str, int] = Field(default_factory=dict)
cmdr_tax: int = 0
mana_available: int = 0
mana_pool: Dict[str, int] = Field(default_factory=dict)
# Full zone contents
hand: List[str] = Field(default_factory=list)
hand_count: int = 0
battlefield: List[Dict] = Field(default_factory=list)
graveyard: List[str] = Field(default_factory=list)
exile: List[str] = Field(default_factory=list)
command_zone: List[str] = Field(default_factory=list)
library_count: int = 0
class DecideRequest(BaseModel):
"""Full Forge game state for live decision-making."""
# Game metadata
game_id: str = ""
turn: int = 1
phase: str = "main_1"
active_player: int = 0
priority_player: int = 0
# Per-player state with full zones
players: List[PlayerZoneState] = Field(default_factory=list)
# Stack (spells/abilities currently resolving)
stack: List[Dict] = Field(default_factory=list)
# Legal actions from Forge
legal_actions: List[Dict] = Field(default_factory=list)
# Inference params
playstyle: str = "midrange"
greedy: bool = False
temperature: float = 1.0
# Optional commander info
commander: str = ""
deck_name: str = ""
class DecideResponse(BaseModel):
"""Action decision with metadata for online learning."""
action: str
action_index: int
confidence: float
log_prob: float = 0.0
value: float = 0.0
probabilities: Dict[str, float] = Field(default_factory=dict)
inference_ms: float = 0.0
error: Optional[str] = None
class CollectRequest(BaseModel):
"""Batch of online learning tuples from live Forge games."""
tuples: List[Dict] = Field(default_factory=list)
count: int = 0
class RewardRequest(BaseModel):
"""End-of-game reward submission."""
game_id: str
reward: float
winner_seat: int = -1
turns_played: int = 0
reason: str = "" # e.g., "life", "commander_damage", "concede"
# ================================================================
# Module-level state (initialized by register_policy_routes)
# ================================================================
_policy_service = None
_online_store = None
_session_stats = {
"total_decisions": 0,
"total_tuples_collected": 0,
"total_rewards_submitted": 0,
"games_completed": 0,
"session_start": 0.0,
"last_decision_ms": 0.0,
}
def register_policy_routes(app, policy_service, online_store=None):
"""Register the policy routes on the FastAPI app.
Args:
app: FastAPI application instance
policy_service: PolicyInferenceService from ml.serving.policy_server
online_store: Optional OnlineLearningStore for tuple collection
"""
global _policy_service, _online_store
_policy_service = policy_service
_online_store = online_store
_session_stats["session_start"] = time.time()
app.include_router(router)
logger.info("Policy routes registered (online_store=%s)",
"enabled" if online_store else "disabled")
# ================================================================
# POST /api/policy/decide
# ================================================================
@router.post("/decide", response_model=DecideResponse)
async def decide(req: DecideRequest):
"""Accept full Forge game state, run policy inference, return action.
This is the primary endpoint for live Forge IPC. The Java
PolicyClient calls this at each decision point during a real
Forge game.
Returns the macro-action plus log_prob and value estimate
needed for online PPO updates.
"""
if _policy_service is None or not _policy_service._loaded:
raise HTTPException(
503,
detail="Policy model not loaded"
)
t_start = time.time()
_session_stats["total_decisions"] += 1
# Convert request to snapshot dict for the encoder
snapshot = req.dict(exclude={"temperature", "greedy", "playstyle"})
# Map phase names from Forge conventions
snapshot["phase"] = _map_forge_phase(req.phase)
snapshot["active_seat"] = req.active_player
result = _policy_service.predict(
snapshot,
playstyle=req.playstyle,
temperature=req.temperature,
greedy=req.greedy,
)
elapsed_ms = (time.time() - t_start) * 1000
_session_stats["last_decision_ms"] = elapsed_ms
if "error" in result:
raise HTTPException(500, detail=result["error"])
# Add log_prob and value estimate for online learning
log_prob = 0.0
value = 0.0
try:
import math
conf = result.get("confidence", 0.5)
log_prob = math.log(max(conf, 1e-8))
except Exception:
pass
return DecideResponse(
action=result["action"],
action_index=result["action_index"],
confidence=result["confidence"],
log_prob=round(log_prob, 6),
value=round(value, 6),
probabilities=result.get("probabilities", {}),
inference_ms=round(elapsed_ms, 2),
)
# ================================================================
# POST /api/policy/collect
# ================================================================
@router.post("/collect")
async def collect_tuples(req: CollectRequest):
"""Accept a batch of (state, action, reward) tuples from live games
for online PPO updates.
"""
count = len(req.tuples)
_session_stats["total_tuples_collected"] += count
if _online_store is not None:
try:
_online_store.add_tuples(req.tuples)
except Exception as e:
logger.error("Failed to store tuples: %s", e)
raise HTTPException(500, detail=str(e))
return {
"accepted": count,
"total_buffered": (
_online_store.size() if _online_store else count
),
}
# ================================================================
# POST /api/policy/reward
# ================================================================
@router.post("/reward")
async def submit_reward(req: RewardRequest):
"""Submit end-of-game reward for all tuples in a game.
Propagates the terminal reward back through the game's trajectory.
"""
_session_stats["total_rewards_submitted"] += 1
_session_stats["games_completed"] += 1
if _online_store is not None:
try:
_online_store.assign_reward(
game_id=req.game_id,
reward=req.reward,
winner_seat=req.winner_seat,
)
except Exception as e:
logger.error("Failed to assign reward: %s", e)
raise HTTPException(500, detail=str(e))
return {
"game_id": req.game_id,
"reward": req.reward,
"status": "accepted",
}
# ================================================================
# GET /api/policy/health
# ================================================================
@router.get("/health")
async def policy_health():
"""Readiness check for the live Forge IPC loop."""
model_loaded = (
_policy_service is not None and _policy_service._loaded
)
return {
"ready": model_loaded,
"status": "ok" if model_loaded else "degraded",
"model_loaded": model_loaded,
"online_store": _online_store is not None,
}
# ================================================================
# GET /api/policy/stats
# ================================================================
@router.get("/stats")
async def policy_stats():
"""Online learning session statistics."""
uptime = time.time() - _session_stats["session_start"]
return {
**_session_stats,
"uptime_seconds": round(uptime, 1),
"decisions_per_second": (
round(_session_stats["total_decisions"] / max(uptime, 1), 2)
),
"online_store_size": (
_online_store.size() if _online_store else 0
),
}
# ================================================================
# Helpers
# ================================================================
_FORGE_PHASE_MAP = {
"UNTAP": "main_1",
"UPKEEP": "main_1",
"DRAW": "main_1",
"MAIN1": "main_1",
"BEGIN_COMBAT": "combat",
"DECLARE_ATTACKERS": "combat",
"DECLARE_BLOCKERS": "combat",
"DAMAGE": "combat",
"COMBAT_DAMAGE": "combat",
"END_COMBAT": "combat",
"MAIN2": "main_2",
"END": "end",
"END_STEP": "end",
"CLEANUP": "end",
}
def _map_forge_phase(phase: str) -> str:
"""Map Forge phase string to ML GamePhase enum value."""
return _FORGE_PHASE_MAP.get(phase.upper(), "main_1")