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| import numpy as np | |
| from engine.game.game_state import GameState | |
| class UnifiedObservationEncoder: | |
| """ | |
| Translates current GameState into various historic observation formats. | |
| """ | |
| def encode(state: GameState, dim: int, player_idx: int = None) -> np.ndarray: | |
| if player_idx is None: | |
| player_idx = state.current_player | |
| if dim == 8192: | |
| return UnifiedObservationEncoder._encode_8192(state, player_idx) | |
| elif dim == 320: | |
| return UnifiedObservationEncoder._encode_320(state, player_idx) | |
| elif dim == 128: | |
| return UnifiedObservationEncoder._encode_128(state, player_idx) | |
| else: | |
| raise ValueError(f"Unsupported observation dimension: {dim}") | |
| def _encode_8192(state: GameState, player_idx: int) -> np.ndarray: | |
| from ai.vector_env import VectorGameState as VGS | |
| from ai.vector_env import encode_observations_vectorized | |
| p = state.players[player_idx] | |
| opp = state.players[1 - player_idx] | |
| # Max ID for normalization is handled inside encoder | |
| # Prepare inputs strictly matching VectorEnv.encode_observations_vectorized signature | |
| # 1. Num Envs (1) | |
| # 2. Batch Hand (1, 60) | |
| # 3. Batch Stage (1, 3) | |
| # 4. Batch Energy Count (1, 3) | |
| # 5. Batch Tapped (1, 3) | |
| # 6. Batch Scores (1,) | |
| # 7. Opp Scores (1,) | |
| # 8. Opp Stage (1, 3) | |
| # 9. Opp Tapped (1, 3) | |
| # 10. Card Stats (from VGS) | |
| # 11. Global Context (1, 128) | |
| # 12. Batch Live (1, 50) | |
| # 13. Batch Opp History (1, 50) | |
| # 14. Turn Number | |
| # 15. Obs Buffer (1, 8192) | |
| # --- Allocations --- | |
| batch_hand = np.zeros((1, 60), dtype=np.int32) | |
| batch_stage = np.full((1, 3), -1, dtype=np.int32) | |
| batch_energy_count = np.zeros((1, 3), dtype=np.int32) | |
| batch_tapped = np.zeros((1, 3), dtype=np.int32) | |
| batch_live = np.zeros((1, 50), dtype=np.int32) | |
| opp_stage = np.full((1, 3), -1, dtype=np.int32) | |
| opp_tapped = np.zeros((1, 3), dtype=np.int32) | |
| opp_history = np.zeros((1, 50), dtype=np.int32) | |
| # --- Population --- | |
| # Hand | |
| h_len = min(len(p.hand), 60) | |
| for i in range(h_len): | |
| batch_hand[0, i] = p.hand[i] | |
| # Stage | |
| for i in range(3): | |
| batch_stage[0, i] = p.stage[i] | |
| batch_energy_count[0, i] = p.stage_energy_count[i] | |
| batch_tapped[0, i] = 1 if p.tapped_members[i] else 0 | |
| opp_stage[0, i] = opp.stage[i] | |
| opp_tapped[0, i] = 1 if opp.tapped_members[i] else 0 | |
| # Live Zone | |
| # Assuming GameState has p.live_zone list or similar? | |
| # GameState definition usually implies 'success_lives' are won lives. | |
| # Active lives might be tracked elsewhere? | |
| # If not available, leave as zeros. | |
| # Checking GameState... usually just has success_lives. Active lives are transient in legacy? | |
| # VectorEnv tracks them. Legacy might not. | |
| # Scores | |
| batch_scores = np.array([len(p.success_lives)], dtype=np.int32) | |
| opp_scores = np.array([len(opp.success_lives)], dtype=np.int32) | |
| # Global Context | |
| g_ctx = np.zeros((1, 128), dtype=np.int32) | |
| g_ctx[0, 0] = len(p.success_lives) # SC | |
| g_ctx[0, 1] = len(opp.success_lives) # OS | |
| g_ctx[0, 2] = len(p.discard) # TR | |
| g_ctx[0, 3] = len(p.hand) # HD | |
| g_ctx[0, 5] = p.energy_count # EN | |
| g_ctx[0, 6] = len(p.main_deck) # DK | |
| g_ctx[0, 8] = 5 # PHASE (Main) - Legacy default | |
| # Opponent History (Trash top cards?) | |
| op_h_len = min(len(opp.discard), 50) | |
| for i in range(op_h_len): | |
| # LIFO? VectorEnv usually assumes LIFO or FIFO depending on implementation. | |
| # Usually end is top. | |
| opp_history[0, i] = opp.discard[-(i + 1)] | |
| # Output buffer | |
| obs = np.zeros((1, 8192), dtype=np.float32) | |
| if not hasattr(UnifiedObservationEncoder, "_vgs_cache"): | |
| UnifiedObservationEncoder._vgs_cache = VGS(1) | |
| vgs = UnifiedObservationEncoder._vgs_cache | |
| encode_observations_vectorized( | |
| 1, | |
| batch_hand, | |
| batch_stage, | |
| batch_energy_count, | |
| batch_tapped, | |
| batch_scores, | |
| opp_scores, | |
| opp_stage, | |
| opp_tapped, | |
| vgs.card_stats, | |
| g_ctx, | |
| batch_live, | |
| opp_history, | |
| state.turn_number, | |
| obs, | |
| ) | |
| return obs[0] | |
| def _encode_320(state: GameState, player_idx: int) -> np.ndarray: | |
| # LEGACY 320 (First Speed-up Era) | |
| # Replicates the encoding from ai/vector_env_legacy.py exactly. | |
| # This era ONLY saw Self Stage and Self Score. Hand/Opp were 0. | |
| obs = np.zeros(320, dtype=np.float32) | |
| p = state.players[player_idx] | |
| max_id_val = 2000.0 # Standard for VectorEnv | |
| # Phase [5] = 1.0 (Mocking Main Phase index from Legacy VectorEnv) | |
| obs[5] = 1.0 | |
| # Current Player [16] | |
| obs[16] = 1.0 | |
| # Stage [168:204] (3 slots * 12 features) | |
| # Note: Hand [36:168] remains 0.0 as in legacy training. | |
| for i in range(3): | |
| cid = p.stage[i] | |
| base = 168 + i * 12 | |
| if cid >= 0: | |
| obs[base] = 1.0 # Exist | |
| obs[base + 1] = cid / max_id_val | |
| # Legacy energy count was normalized by 5.0 | |
| obs[base + 11] = min(p.stage_energy_count[i] / 5.0, 1.0) | |
| # Score [270] (Self Score normalized by 5.0 in legacy) | |
| obs[270] = min(len(p.success_lives) / 5.0, 1.0) | |
| return obs | |
| def _encode_128(state: GameState, player_idx: int) -> np.ndarray: | |
| # 128-dim is the global_ctx vector | |
| p = state.players[player_idx] | |
| opp = state.players[1 - player_idx] | |
| g_ctx = np.zeros(128, dtype=np.float32) | |
| # Standard normalization from AlphaZero era | |
| g_ctx[0] = len(p.success_lives) / 3.0 | |
| g_ctx[1] = len(opp.success_lives) / 3.0 | |
| g_ctx[2] = len(p.discard) / 50.0 | |
| g_ctx[3] = len(p.hand) / 50.0 # Normalized to deck size usually | |
| g_ctx[5] = p.energy_count / 10.0 | |
| g_ctx[6] = len(p.main_deck) / 50.0 | |
| # Turn info | |
| g_ctx[10] = state.turn_number / 20.0 | |
| g_ctx[11] = 1.0 if state.current_player == player_idx else 0.0 | |
| return g_ctx | |