63 lines
2.5 KiB
Python
63 lines
2.5 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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# torch.save(model.state_dict(), model_path.replace(".ckpt", "_nobn.ckpt"))
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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 "resnet" in model_path else "old"
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self.model = _load_model(position, model_path, self.model_type)
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self.EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',
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8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',
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13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'}
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def act(self, infoset):
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if len(infoset.legal_actions) == 1:
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return infoset.legal_actions[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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# action_list = []
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# output = ""
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# for i, action in enumerate(y_pred):
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# action_list.append((y_pred[i].item(), "".join([self.EnvCard2RealCard[ii] for ii in infoset.legal_actions[i]]) if len(infoset.legal_actions[i]) != 0 else "Pass"))
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# action_list.sort(key=lambda x: x[0], reverse=True)
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# value_list = []
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# for action in action_list:
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# output += str(round(action[0],3)) + " " + action[1] + "\n"
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# value_list.append(action[0])
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# # print(value_list)
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# print(output)
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# print("--------------------\n")
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return best_action
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