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update for torch 0.2

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Morvan Zhou
2017-09-05 12:57:25 +10:00
committed by Morvan Zhou
parent f31c88b81e
commit 3b23f6bced
1 changed files with 2 additions and 2 deletions
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tutorial-contents/405_DQN_Reinforcement_learning.py
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@ -58,7 +58,7 @@ class DQN(object):
# input only one sample # input only one sample
if np.random.uniform() < EPSILON: # greedy if np.random.uniform() < EPSILON: # greedy
actions_value = self.eval_net.forward(x) actions_value = self.eval_net.forward(x)
action = torch.max(actions_value, 1)[1].data.numpy()[0, 0] # return the argmax action = torch.max(actions_value, 1)[1].data.numpy()[0] # return the argmax
else: # random else: # random
action = np.random.randint(0, N_ACTIONS) action = np.random.randint(0, N_ACTIONS)
return action return action
@ -87,7 +87,7 @@ class DQN(object):
# q_eval w.r.t the action in experience # q_eval w.r.t the action in experience
q_eval = self.eval_net(b_s).gather(1, b_a) # shape (batch, 1) q_eval = self.eval_net(b_s).gather(1, b_a) # shape (batch, 1)
q_next = self.target_net(b_s_).detach() # detach from graph, don't backpropagate q_next = self.target_net(b_s_).detach() # detach from graph, don't backpropagate
q_target = b_r + GAMMA * q_next.max(1)[0] # shape (batch, 1) q_target = b_r + GAMMA * q_next.max(1)[0].view(BATCH_SIZE, 1) # shape (batch, 1)
loss = self.loss_func(q_eval, q_target) loss = self.loss_func(q_eval, q_target)
self.optimizer.zero_grad() self.optimizer.zero_grad()