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Major reversion

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This commit is contained in:
Shangtong Zhang
2017-07-30 13:24:04 -06:00
parent ce504e2d0f
commit bbfcbf76f4
7 changed files with 131 additions and 110 deletions
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async_worker/actor_critic.py
+15 -10
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@@ -9,13 +9,14 @@ from torch.autograd import Variable
import torch.nn as nn
class AdvantageActorCritic:
def __init__(self, config):
def __init__(self, config, learning_network, target_network):
self.config = config
self.optimizer = config.optimizer_fn(config.learning_network.parameters())
self.optimizer = config.optimizer_fn(learning_network.parameters())
self.worker_network = config.network_fn()
self.worker_network.load_state_dict(config.learning_network.state_dict())
self.worker_network.load_state_dict(learning_network.state_dict())
self.task = config.task_fn()
self.policy = config.policy_fn()
self.learning_network = learning_network
def episode(self, deterministic=False):
config = self.config
@@ -51,12 +52,16 @@ class AdvantageActorCritic:
GAE = torch.FloatTensor([[0]])
for i in reversed(range(len(pending))):
prob, log_prob, value, action, reward = pending[i]
R = reward + config.discount * R
advantage = Variable(R) - value
GAE = config.discount * GAE + advantage.data
loss += 0.5 * advantage.pow(2)
if i == len(pending) - 1:
delta = reward + config.discount * R - value.data
else:
delta = reward + pending[i + 1][2].data - value.data
GAE = config.discount * config.gae_tau * GAE + delta
loss += -log_prob.gather(1, Variable(torch.LongTensor([[action]]))) * Variable(GAE)
loss += 0.01 * torch.sum(torch.mul(prob, log_prob))
loss += config.entropy_weight * torch.sum(torch.mul(prob, log_prob))
R = reward + config.discount * R
loss += 0.5 * (Variable(R) - value).pow(2)
pending = []
self.worker_network.zero_grad()
@@ -64,12 +69,12 @@ class AdvantageActorCritic:
loss.backward()
nn.utils.clip_grad_norm(self.worker_network.parameters(), config.gradient_clip)
for param, worker_param in zip(
config.learning_network.parameters(), self.worker_network.parameters()):
self.learning_network.parameters(), self.worker_network.parameters()):
if param.grad is not None:
break
param._grad = worker_param.grad
self.optimizer.step()
self.worker_network.load_state_dict(config.learning_network.state_dict())
self.worker_network.load_state_dict(self.learning_network.state_dict())
self.worker_network.reset(terminal)
if terminal: