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| """ | |
| Self-play orchestration for o1 agent. | |
| Runs self-play games using MCTS for move selection. | |
| """ | |
| import chess | |
| from o1.mcts import MCTS | |
| def run_selfplay(agent, num_games=1, simulations=50): | |
| """Run self-play games using MCTS and return experience.""" | |
| all_experience = [] | |
| for game_idx in range(num_games): | |
| board = chess.Board() | |
| mcts = MCTS(agent, simulations=simulations) | |
| game_data = [] | |
| while not board.is_game_over(): | |
| move = mcts.search(board) | |
| state_tensor = agent.board_to_tensor(board) | |
| # Policy: one-hot for chosen move (for now) | |
| policy = [0] * 4672 # 4672 is max legal moves in chess | |
| move_idx = list(board.legal_moves).index(move) | |
| policy[move_idx] = 1 | |
| value = 0 # Placeholder, will be set after game | |
| game_data.append((state_tensor, policy, value)) | |
| board.push(move) | |
| # Assign final result as value for all positions | |
| result = board.result() | |
| if result == '1-0': | |
| z = 5 | |
| elif result == '0-1': | |
| z = -1 | |
| else: | |
| z = 0 | |
| game_data = [(s, p, z) for (s, p, v) in game_data] | |
| all_experience.extend(game_data) | |
| return all_experience | |
| # Self-play loop implementation will go here | |