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let ppo work with gpu

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wassname
2017-11-15 09:40:58 +08:00
parent aec67c66f8
commit e16a560abc
1 changed files with 9 additions and 6 deletions
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async_worker/ppo.py
+9 -6
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@@ -65,7 +65,7 @@ class ProximalPolicyOptimization:
for i in range(config.rollout_length):
mean, std, log_std = actor_net.predict(np.stack([state]))
value = critic_net.predict(np.stack([state]))
action = self.policy.sample(mean.data.numpy().flatten(), std.data.numpy().flatten(), deterministic)
action = self.policy.sample(mean.data.cpu().numpy().flatten(), std.data.cpu().numpy().flatten(), deterministic)
action = self.config.action_shift_fn(action)
states.append(state)
actions.append(action)
@@ -92,12 +92,15 @@ class ProximalPolicyOptimization:
if not done:
R = critic_net.predict(np.stack([state])).data
values.append(Variable(R))
A = Variable(torch.zeros((1, 1)))
values.append(actor_net.to_torch_variable(R))
A = actor_net.to_torch_variable(torch.zeros((1, 1)))
discount = actor_net.to_torch_variable([self.config.discount])
gae_tau = actor_net.to_torch_variable([self.config.gae_tau])
for i in reversed(range(len(rewards))):
R = Variable(torch.FloatTensor([[rewards[i]]]))
ret = R + self.config.discount * values[i + 1]
A = ret - values[i] + self.config.discount * self.config.gae_tau * A
R = actor_net.to_torch_variable([[rewards[i]]])
ret = R + discount * values[i + 1]
A = ret - values[i] + discount * gae_tau * A
advantages.append(A.detach())
returns.append(ret.detach())
advantages = list(reversed(advantages))