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alphago_policy_rl.py

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+import torch
+from tqdm import tqdm
+
+from tugo.agent.pg import PolicyAgent
+from tugo.agent.predict import DeepLearningAgent
+from tugo.encoders.alphago import AlphaGoEncoder
+from tugo.rl.simulate import experience_simulation
+
+encoder = AlphaGoEncoder()
+
+# Load opponents
+sl_agent = DeepLearningAgent.load('checkpoints/alphago_sl_policy.pt', encoder)
+sl_opponent = DeepLearningAgent.load('checkpoints/alphago_sl_policy.pt', encoder)
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+alphago_rl_agent = PolicyAgent(sl_agent.model.to(device), encoder)
+opponent = PolicyAgent(sl_opponent.model.to(device), encoder)
+
+# Run simulation
+num_games = 1000
+
+with tqdm(total=num_games) as pbar:
+    experience = experience_simulation(num_games, alphago_rl_agent, opponent, progress_callback=lambda: pbar.update(1))
+
+alphago_rl_agent.train(experience)
+
+# # Serialize RL agent
+# alphago_rl_agent.save('checkpoints/alphago_rl_policy.pt')
+
+# # Serialize experience
+# experience.save('checkpoints/alphago_rl_experience.pt')
+
+
+
+def generate_incremental_filepath(file_path):
+    import os
+    directory, filename = os.path.split(file_path)
+    filename_base, file_extension = os.path.splitext(filename)
+
+    # 查找保存目录下的最后一个文件序号
+    last_index = 0
+    file_exists = False
+
+    for file_name in os.listdir(directory):
+        if file_name.startswith(filename_base) and file_name.endswith(file_extension):
+            try:
+                index = int(file_name[len(filename_base):-len(file_extension)])
+                last_index = max(last_index, index)
+                file_exists = True
+            except ValueError:
+                continue
+
+    if not file_exists:
+        last_index = 0
+
+    # 生成带序号的新文件名
+    new_index = last_index + 1
+    new_filename = f"{filename_base}_{new_index}{file_extension}"
+
+    # 生成新文件的完整路径
+    new_file_path = os.path.join(directory, new_filename)
+
+    return new_file_path
+
+
+# Serialize RL agent
+alphago_rl_agent.save(generate_incremental_filepath('checkpoints/alphago_rl_policy.pt'))
+
+# Serialize experience
+experience.save(generate_incremental_filepath('checkpoints/alphago_rl_experience.pt'))
+