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Specify a gpu for a network

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Shangtong Zhang committed 2018-02-03 10:53:14 -07:00
1 parent fef04b6bf6
commit e91ec3be45
12 files changed
+93 -103
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async_worker/ppo.py
+5 -5
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@@ -92,10 +92,10 @@ class ProximalPolicyOptimization:
R = critic_net.predict(np.stack([state])).data
values.append(actor_net.to_torch_variable(R))
A = actor_net.to_torch_variable(torch.zeros((1, 1)))
values.append(actor_net.variable(R))
A = actor_net.variable(torch.zeros((1, 1)))
for i in reversed(range(len(rewards))):
R = actor_net.to_torch_variable([[rewards[i]]])
R = actor_net.variable([[rewards[i]]])
ret = R + self.config.discount * values[i + 1]
A = ret - values[i] + self.config.discount * self.config.gae_tau * A
advantages.append(A.detach())
@@ -123,8 +123,8 @@ class ProximalPolicyOptimization:
self.worker_network.load_state_dict(self.shared_network.state_dict())
states, actions, returns, advantages = replay.sample()
states = actor_net.to_torch_variable(np.stack(states))
actions = actor_net.to_torch_variable(np.stack(actions))
states = actor_net.variable(np.stack(states))
actions = actor_net.variable(np.stack(actions))
returns = torch.cat(returns, 0)
advantages = torch.cat(advantages, 0).squeeze(1)
advantages = (advantages - advantages.mean()) / advantages.std()