112 lines
4.5 KiB
Python
112 lines
4.5 KiB
Python
import numpy as np
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import torch
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import torch.optim as optim
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from tugo import encoders
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# from tugo.game_logic import goboard
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from tugo.game_logic import goboard_fast as goboard
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from tugo.agents import Agent
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from tugo.agents.helpers import is_point_an_eye
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class ACAgent(Agent):
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def __init__(self, model, encoder):
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super().__init__()
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self.model = model
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self.encoder = encoder
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self.collector = None
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self.temperature = 1.0
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self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
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self.model = self.model.to(self.device)
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self.last_state_value = 0
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def set_temperature(self, temperature):
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self.temperature = temperature
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def set_collector(self, collector):
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self.collector = collector
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def select_move(self, game_state):
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num_moves = self.encoder.board_width * self.encoder.board_height
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board_tensor = self.encoder.encode(game_state)
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x = torch.tensor([board_tensor], dtype=torch.float32).to(self.device)
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actions, values = self.model(x)
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move_probs = actions[0].cpu().detach().numpy()
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estimated_value = values[0][0].cpu().detach().numpy()
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self.last_state_value = float(estimated_value)
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# Prevent move probs from getting stuck at 0 or 1.
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move_probs = np.power(move_probs, 1.0 / self.temperature)
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move_probs = move_probs / np.sum(move_probs)
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eps = 1e-6
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move_probs = np.clip(move_probs, eps, 1 - eps)
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# Re-normalize to get another probability distribution.
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move_probs = move_probs / np.sum(move_probs)
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# Turn the probabilities into a ranked list of moves.
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candidates = np.arange(num_moves)
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ranked_moves = np.random.choice(
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candidates, num_moves, replace=False, p=move_probs)
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for point_idx in ranked_moves:
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point = self.encoder.decode_point_index(point_idx)
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if game_state.is_valid_move(goboard.Move.play(point)) and \
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not is_point_an_eye(game_state.board,
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point,
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game_state.next_player):
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if self.collector is not None:
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self.collector.record_decision(
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state=board_tensor,
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action=point_idx,
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estimated_value=estimated_value
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)
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return goboard.Move.play(point)
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# No legal, non-self-destructive moves less.
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return goboard.Move.pass_turn()
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def train(self, experience, lr=0.1, batch_size=128):
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opt = optim.Adam(self.model.parameters(), lr=lr)
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n = experience.states.shape[0]
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num_moves = self.encoder.num_points()
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policy_target = np.zeros((n, num_moves))
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value_target = np.zeros((n,))
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for i in range(n):
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action = experience.actions[i]
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reward = experience.rewards[i]
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policy_target[i][action] = experience.advantages[i]
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value_target[i] = reward
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policy_target = torch.tensor(policy_target, dtype=torch.float32).to(self.device)
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value_target = torch.tensor(value_target, dtype=torch.float32).to(self.device)
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states = torch.tensor(experience.states, dtype=torch.float32).to(self.device)
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for _ in range(n // batch_size):
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self.model.train()
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opt.zero_grad()
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action_pred, value_pred = self.model(states)
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value_loss = ((value_target - value_pred) ** 2).mean()
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policy_loss = -(policy_target * torch.log(action_pred)).sum(1).mean()
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loss = policy_loss + value_loss
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loss.backward()
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opt.step()
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def diagnostics(self):
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return {'value': self.last_state_value}
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def save(self, file_path):
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torch.save(self.model.state_dict(), file_path)
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@classmethod
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def load(cls, file_path, encoder):
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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model = AlphaGoModel(encoder.get_input_shape(), is_policy_net=True).to(device)
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model.load_state_dict(torch.load(file_path))
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return cls(model, encoder)
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# def load_ac_agent(path, encoder):
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# # 在这个转换中，MyModel() 是你的 PyTorch 模型的实例化方法，你需要将其替换为你实际使用的模型。并且，假设你的模型接受一个状态并输出行动和值。
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# model = MyModel() # Initialize your model
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# model.load_state_dict(torch.load(path))
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# return ACAgent(model, encoder)
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