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82 lines
3.3 KiB
Python
82 lines
3.3 KiB
Python
#######################################################################
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# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
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# Permission given to modify the code as long as you keep this #
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# declaration at the top #
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#######################################################################
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import numpy as np
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import torch
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from torch.autograd import Variable
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import torch.nn as nn
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class NStepQLearning:
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def __init__(self, config, learning_network, target_network):
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self.config = config
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self.optimizer = config.optimizer_fn(learning_network.parameters())
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self.worker_network = config.network_fn()
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self.worker_network.load_state_dict(learning_network.state_dict())
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self.task = config.task_fn()
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self.policy = config.policy_fn()
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self.learning_network = learning_network
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self.target_network = target_network
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def episode(self, deterministic=False):
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config = self.config
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state = self.task.reset()
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steps = 0
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total_reward = 0
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pending = []
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while not config.stop_signal.value:
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q = self.worker_network.predict(np.stack([state]))
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action = self.policy.sample(q.data.numpy().flatten(), deterministic)
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next_state, reward, terminal, _ = self.task.step(action)
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terminal = (terminal or (config.max_episode_length and steps >= config.max_episode_length))
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steps += 1
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total_reward += reward
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if deterministic:
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if terminal:
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break
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state = next_state
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continue
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with config.steps_lock:
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config.total_steps.value += 1
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pending.append([q, action, reward])
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if terminal or len(pending) >= config.update_interval:
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loss = 0
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if terminal:
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R = torch.FloatTensor([0])
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else:
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R, _ = self.target_network.predict(
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np.stack([next_state])).data.max(1)
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for i in reversed(range(len(pending))):
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q, action, reward = pending[i]
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R = reward + config.discount * R
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q = q.gather(1, Variable(torch.LongTensor([[action]]))).unsqueeze(1)
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loss += 0.5 * (Variable(R) - q).pow(2)
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pending = []
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self.worker_network.zero_grad()
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self.optimizer.zero_grad()
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loss.backward()
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nn.utils.clip_grad_norm(self.worker_network.parameters(), config.gradient_clip)
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for param, worker_param in zip(
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self.learning_network.parameters(), self.worker_network.parameters()):
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if param.grad is not None:
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break
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param._grad = worker_param.grad
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self.optimizer.step()
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self.worker_network.load_state_dict(self.learning_network.state_dict())
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self.worker_network.reset(terminal)
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if terminal:
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break
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state = next_state
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if config.total_steps.value % config.target_network_update_freq == 0:
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self.target_network.load_state_dict(self.learning_network.state_dict())
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return steps, total_reward |