50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
import torch
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import numpy as np
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from douzero.env.env import get_obs
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def _load_model(position, model_path, model_type):
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from douzero.dmc.models import model_dict_new, model_dict
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model = None
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if model_type == "general":
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model = model_dict_new[position]()
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else:
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model = model_dict[position]()
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model_state_dict = model.state_dict()
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if torch.cuda.is_available():
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pretrained = torch.load(model_path, map_location='cuda:0')
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else:
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pretrained = torch.load(model_path, map_location='cpu')
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pretrained = {k: v for k, v in pretrained.items() if k in model_state_dict}
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model_state_dict.update(pretrained)
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model.load_state_dict(model_state_dict)
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if torch.cuda.is_available():
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model.cuda()
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model.eval()
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return model
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class DeepAgent:
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def __init__(self, position, model_path):
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self.model_type = "general" if "general" in model_path else "old"
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self.model = _load_model(position, model_path, self.model_type)
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def act(self, infoset):
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# 只有一个合法动作时直接返回，这样会得不到胜率信息
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# if len(infoset.legal_actions) == 1:
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# return infoset.legal_actions[0], 0
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obs = get_obs(infoset, self.model_type == "general")
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z_batch = torch.from_numpy(obs['z_batch']).float()
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x_batch = torch.from_numpy(obs['x_batch']).float()
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if torch.cuda.is_available():
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z_batch, x_batch = z_batch.cuda(), x_batch.cuda()
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y_pred = self.model.forward(z_batch, x_batch, return_value=True)['values']
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y_pred = y_pred.detach().cpu().numpy()
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best_action_index = np.argmax(y_pred, axis=0)[0]
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best_action = infoset.legal_actions[best_action_index]
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best_action_confidence = y_pred[best_action_index]
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# print(best_action, best_action_confidence, y_pred)
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return best_action, best_action_confidence
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