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96 lines
4.1 KiB
Python
96 lines
4.1 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 ContinuousAdvantageActorCritic:
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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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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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pi = Variable(torch.FloatTensor([np.pi]))
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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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mean, var, value = self.worker_network.predict(np.stack([state]))
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action = self.policy.sample(mean.data.numpy().flatten(),
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var.data.numpy().flatten(),
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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 not deterministic:
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reward = np.clip(reward, -1, 1)
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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([mean, var, 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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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_network.critic(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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mean, var, value, action, reward = pending[i]
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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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action = Variable(torch.FloatTensor([action]))
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prob_part1 = (-(action - mean).pow(2) / (2 * var)).exp()
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prob_part2 = 1 / (2 * var * pi.expand_as(var)).sqrt()
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prob = prob_part1 * prob_part2
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log_prob = prob.log()
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loss += -torch.sum(log_prob) * Variable(GAE)
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entropy = 0.5 * (1.0 + (var * 2 * pi.expand_as(var)).log()).sum()
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loss += config.entropy_weight * entropy
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R = reward + config.discount * R
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loss += 0.5 * (Variable(R) - value).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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return steps, total_reward |