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This commit is contained in:
Dobromir Popov
2025-07-27 20:56:37 +03:00
parent bd986f4534
commit 9e1684f9f8
7 changed files with 531 additions and 112 deletions
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24
NN/models/dqn_agent.py
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@ -271,10 +271,16 @@ class DQNAgent:
else:
self.device = device
logger.info(f"DQN Agent using device: {self.device}")
# Initialize models with RL-specific network architecture
self.policy_net = DQNNetwork(self.state_dim, self.n_actions).to(self.device)
self.target_net = DQNNetwork(self.state_dim, self.n_actions).to(self.device)
# Ensure models are on the correct device
self.policy_net = self.policy_net.to(self.device)
self.target_net = self.target_net.to(self.device)
# Initialize the target network with the same weights as the policy network
self.target_net.load_state_dict(self.policy_net.state_dict())
@ -997,11 +1003,19 @@ class DQNAgent:
# Convert to tensors with proper validation
try:
states = torch.FloatTensor(np.array(states)).to(self.device)
actions = torch.LongTensor(np.array(actions)).to(self.device)
rewards = torch.FloatTensor(np.array(rewards)).to(self.device)
next_states = torch.FloatTensor(np.array(next_states)).to(self.device)
dones = torch.FloatTensor(np.array(dones)).to(self.device)
# Ensure all data is on CPU first, then move to device
states_array = np.array(states, dtype=np.float32)
actions_array = np.array(actions, dtype=np.int64)
rewards_array = np.array(rewards, dtype=np.float32)
next_states_array = np.array(next_states, dtype=np.float32)
dones_array = np.array(dones, dtype=np.float32)
# Convert to tensors and move to device
states = torch.from_numpy(states_array).to(self.device)
actions = torch.from_numpy(actions_array).to(self.device)
rewards = torch.from_numpy(rewards_array).to(self.device)
next_states = torch.from_numpy(next_states_array).to(self.device)
dones = torch.from_numpy(dones_array).to(self.device)
# Final validation of tensor shapes
if states.shape[0] == 0 or actions.shape[0] == 0: