ZYM/PPCA-AIPacMan-2024
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

rein q7

Browse Source
This commit is contained in:
ZhuangYumin
2024-07-08 23:42:48 +08:00
parent a825670fda
commit 1bf4cc1efe
2 changed files with 37 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
reinforcement/deepQLearningAgents.py
View File

@ -97,15 +97,15 @@ class PacmanDeepQAgent(PacmanQAgent):
next_states = torch.tensor(next_states)
done = np.array([x.done for x in minibatch])
Q_predict = network.run(states).data.detach().numpy()
Q_predict = network.run(states).data.detach().cpu().numpy()
Q_target = np.copy(Q_predict )
state_indices = states.int().detach().numpy()
state_indices = (state_indices[:, 0], state_indices[:, 1])
exploration_bonus = 1 / (2 * np.sqrt((self.counts[state_indices] / 100)))
replace_indices = np.arange(actions.shape[0])
action_indices = np.argmax(network.run(next_states).data, axis=1)
target = rewards + exploration_bonus + (1 - done) * self.discount * target_network.run(next_states).data[replace_indices, action_indices].detach().numpy()
action_indices = np.argmax(network.run(next_states).data.cpu(), axis=1)
target = rewards + exploration_bonus + (1 - done) * self.discount * target_network.run(next_states).data[replace_indices, action_indices].detach().cpu().numpy()
Q_target[replace_indices, actions] = target