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106 lines
4.7 KiB
Python
106 lines
4.7 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 AdvantageActorCritic:
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def __init__(self, config, actor, critic):
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self.config = config
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self.actor_opt = torch.optim.SGD(actor.parameters(), lr=0.0001)
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self.critic_opt = torch.optim.SGD(critic.parameters(), lr=0.0001)
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# self.optimizer = config.optimizer_fn(learning_network.parameters())
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self.worker_actor = config.actor_fn()
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self.worker_actor.load_state_dict(actor.state_dict())
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self.worker_critic = config.critic_fn()
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self.worker_critic.load_state_dict(critic.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.actor = actor
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self.critic = critic
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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 and \
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(not config.max_episode_length or steps < config.max_episode_length):
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prob, log_prob = self.actor.predict(np.stack([state]))
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value = self.critic.predict(np.stack([state]))
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# prob, log_prob, value = self.worker_network.predict(np.stack([state]))
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action = self.policy.sample(prob.data.numpy().flatten(), deterministic)
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next_state, reward, terminal, _ = self.task.step(action)
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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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pending.append([prob, log_prob, value, action, reward])
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with config.steps_lock:
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config.total_steps.value += 1
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if terminal or len(pending) >= config.update_interval:
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policy_loss = 0
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value_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.worker_critic.predict(np.stack([next_state])).data
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GAE = torch.FloatTensor([[0]])
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for i in reversed(range(len(pending))):
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prob, log_prob, value, action, reward = pending[i]
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R = config.discount * R + reward
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GAE = R - value.data
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# if i == len(pending) - 1:
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# delta = reward + config.discount * R - value.data
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# else:
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# delta = reward + pending[i + 1][2].data - value.data
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# GAE = config.discount * config.gae_tau * GAE + delta
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policy_loss += -log_prob.gather(1, Variable(torch.LongTensor([[action]]))) * Variable(GAE)
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policy_loss += config.entropy_weight * torch.sum(torch.mul(prob, log_prob))
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R = reward + config.discount * R
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value_loss += 0.5 * (Variable(R) - value).pow(2)
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pending = []
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self.worker_actor.zero_grad()
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self.actor_opt.zero_grad()
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policy_loss.backward()
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nn.utils.clip_grad_norm(self.worker_actor.parameters(), config.gradient_clip)
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for param, worker_param in zip(
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self.actor.parameters(), self.worker_actor.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.actor_opt.step()
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self.worker_actor.load_state_dict(self.actor.state_dict())
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# self.worker_network.reset(terminal)
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self.worker_critic.zero_grad()
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self.critic_opt.zero_grad()
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value_loss.backward()
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nn.utils.clip_grad_norm(self.worker_critic.parameters(), config.gradient_clip)
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for param, worker_param in zip(
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self.critic.parameters(), self.worker_critic.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.critic_opt.step()
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self.worker_critic.load_state_dict(self.critic.state_dict())
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if terminal:
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break
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state = next_state
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return steps, total_reward |