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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/n_step_q.py
+9 -7
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@@ -9,13 +9,15 @@ from torch.autograd import Variable
import torch.nn as nn
class NStepQLearning:
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
self.target_network = target_network
def episode(self, deterministic=False):
config = self.config
@@ -47,7 +49,7 @@ class NStepQLearning:
if terminal:
R = torch.FloatTensor([[0]])
else:
R, _ = config.target_network.predict(
R, _ = self.target_network.predict(
np.stack([next_state])).data.max(1)
for i in reversed(range(len(pending))):
@@ -61,12 +63,12 @@ class NStepQLearning:
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:
@@ -74,6 +76,6 @@ class NStepQLearning:
state = next_state
if config.total_steps.value % config.target_network_update_freq == 0:
config.target_network.load_state_dict(config.learning_network.state_dict())
self.target_network.load_state_dict(self.learning_network.state_dict())
return steps, total_reward