update save and load

rl/ac_pass.py

@@ -0,0 +1,111 @@
+import torch
+import numpy as np
+
+from tugo import encoders
+from tugo.game_logic import goboard
+from tugo.agents import Agent
+
+
+class ACAgent(Agent):
+    def __init__(self, model, encoder):
+        Agent.__init__(self)
+        self.model = model
+        self.encoder = encoder
+        self.collector = None
+        self.temperature = 1.0
+
+        self.last_state_value = 0
+
+    def set_temperature(self, temperature):
+        self.temperature = temperature
+
+    def set_collector(self, collector):
+        self.collector = collector
+
+    def select_move(self, game_state):
+        num_moves = self.encoder.board_width * self.encoder.board_height
+
+        board_tensor = self.encoder.encode(game_state)
+        x = torch.from_numpy(np.array([board_tensor])).float().to(self.model.device)
+
+        actions, values = self.model(x)
+        move_probs = actions[0].detach().cpu().numpy()
+        estimated_value = values[0][0].item()
+        self.last_state_value = float(estimated_value)
+
+        # Prevent move probs from getting stuck at 0 or 1.
+        move_probs = np.power(move_probs, 1.0 / self.temperature)
+        move_probs = move_probs / np.sum(move_probs)
+        eps = 1e-6
+        move_probs = np.clip(move_probs, eps, 1 - eps)
+        # Re-normalize to get another probability distribution.
+        move_probs = move_probs / np.sum(move_probs)
+
+        # Turn the probabilities into a ranked list of moves.
+        candidates = np.arange(num_moves)
+        ranked_moves = np.random.choice(
+            candidates, num_moves, replace=False, p=move_probs)
+        for point_idx in ranked_moves:
+            point = self.encoder.decode_point_index(point_idx)
+            true_move = goboard.Move.play(point)
+            if not game_state.is_valid_move(true_move):
+                true_move = goboard.Move.pass_turn()
+            if self.collector is not None:
+                self.collector.record_decision(
+                    state=board_tensor,
+                    action=point_idx,
+                    estimated_value=estimated_value
+                )
+            return true_move
+        # No legal, non-self-destructive moves less.
+        return goboard.Move.pass_turn()
+
+    def train(self, experience, lr=0.1, batch_size=128):
+        optimizer = torch.optim.SGD(self.model.parameters(), lr=lr, clipvalue=0.2)
+        criterion = torch.nn.CrossEntropyLoss()
+
+        n = experience.states.shape[0]
+        num_moves = self.encoder.num_points()
+        policy_target = np.zeros((n, num_moves))
+        value_target = np.zeros((n,))
+        for i in range(n):
+            action = experience.actions[i]
+            reward = experience.rewards[i]
+            policy_target[i][action] = experience.advantages[i]
+            value_target[i] = reward
+
+        self.model.train()
+        optimizer.zero_grad()
+        policy_target_t = torch.from_numpy(policy_target).float().to(self.model.device)
+        value_target_t = torch.from_numpy(value_target).float().to(self.model.device)
+        states_t = torch.from_numpy(experience.states).float().to(self.model.device)
+        actions_pred, values_pred = self.model(states_t)
+        loss1 = criterion(actions_pred, policy_target_t)
+        loss2 = criterion(values_pred, value_target_t)
+        loss = loss1 + loss2
+        loss.backward()
+        optimizer.step()
+
+    def serialize(self, file_path):
+        torch.save({
+            'model_state_dict': self.model.state_dict(),
+            'encoder_name': self.encoder.name(),
+            'encoder_board_width': self.encoder.board_width,
+            'encoder_board_height': self.encoder.board_height,
+        }, file_path)
+
+    def diagnostics(self):
+        return {'value': self.last_state_value}
+
+
+def load_passing_ac_agent(file_path):
+    checkpoint = torch.load(file_path)
+    model = TheModelClass()  # TheModelClass should be replaced with your model class
+    model.load_state_dict(checkpoint['model_state_dict'])
+    encoder_name = checkpoint['encoder_name']
+    board_width = checkpoint['encoder_board_width']
+    board_height = checkpoint['encoder_board_height']
+    encoder = encoders.get_encoder_by_name(
+        encoder_name,
+        (board_width, board_height))
+    return ACAgent(model, encoder)