ZYM/PPCA-AIPacMan-2024
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ZhuangYumin
2024-07-22 01:33:54 +08:00
parent 25f46c9a13
commit 1f217770b7
2 changed files with 4 additions and 1 deletions
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3
reinforcement/deepQLearningAgents.py
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@ -73,6 +73,9 @@ class PacmanDeepQAgent(PacmanQAgent):
else: else:
print("Initializing new model parameters") print("Initializing new model parameters")
def save_model(self, filename="para.bin"): def save_model(self, filename="para.bin"):
if model.kProductionMode:
print("in production mode, not saving model")
return
print(f"Saving model parameters to {filename}") print(f"Saving model parameters to {filename}")
torch.save({ torch.save({
'model_state_dict': self.model.state_dict(), 'model_state_dict': self.model.state_dict(),

2
reinforcement/model.py
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@ -11,7 +11,7 @@ from torch import tensor, double, optim
from torch.nn.functional import relu, mse_loss from torch.nn.functional import relu, mse_loss
import torch import torch
kProductionMode=True kProductionMode=False
class DeepQNetwork(Module): class DeepQNetwork(Module):
""" """
A model that uses a Deep Q-value Network (DQN) to approximate Q(s,a) as part A model that uses a Deep Q-value Network (DQN) to approximate Q(s,a) as part