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110 lines
4.4 KiB
Python
110 lines
4.4 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.multiprocessing as mp
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from network import *
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from utils import *
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from component import *
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import pickle
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import os
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import time
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import gym.monitoring
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class DDPGAgent:
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def __init__(self, config):
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self.config = config
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self.task = config.task_fn()
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self.worker_network = config.network_fn()
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self.target_network = config.network_fn()
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self.target_network.load_state_dict(self.worker_network.state_dict())
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self.actor_opt = config.actor_optimizer_fn(self.worker_network.actor.parameters())
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self.critic_opt = config.critic_optimizer_fn(self.worker_network.critic.parameters())
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self.replay = config.replay_fn()
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self.random_process = config.random_process_fn()
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self.criterion = nn.MSELoss()
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self.total_steps = 0
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self.state_normalizer = Normalizer(self.task.state_dim)
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self.reward_normalizer = Normalizer(1)
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def soft_update(self, target, src):
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for target_param, param in zip(target.parameters(), src.parameters()):
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target_param.data.copy_(target_param.data * (1.0 - self.config.target_network_mix) +
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param.data * self.config.target_network_mix)
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def save(self, file_name):
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with open(file_name, 'wb') as f:
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torch.save(self.worker_network.state_dict(), f)
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def episode(self, deterministic=False, video_recorder=None):
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self.random_process.reset_states()
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state = self.task.reset()
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state = self.state_normalizer(state)
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config = self.config
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actor = self.worker_network.actor
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critic = self.worker_network.critic
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target_actor = self.target_network.actor
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target_critic = self.target_network.critic
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steps = 0
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total_reward = 0.0
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while True:
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actor.eval()
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action = actor.predict(np.stack([state])).flatten()
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if not deterministic:
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action += self.random_process.sample()
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next_state, reward, done, info = self.task.step(action)
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if video_recorder is not None:
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video_recorder.capture_frame()
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done = (done or (config.max_episode_length and steps >= config.max_episode_length))
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next_state = self.state_normalizer(next_state)
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total_reward += reward
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reward = self.reward_normalizer(reward)
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if not deterministic:
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self.replay.feed([state, action, reward, next_state, int(done)])
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self.total_steps += 1
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steps += 1
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state = next_state
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if done:
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break
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if not deterministic and self.replay.size() >= config.min_memory_size:
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self.worker_network.train()
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experiences = self.replay.sample()
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states, actions, rewards, next_states, terminals = experiences
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q_next = target_critic.predict(next_states, target_actor.predict(next_states))
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terminals = critic.to_torch_variable(terminals).unsqueeze(1)
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rewards = critic.to_torch_variable(rewards).unsqueeze(1)
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q_next = config.discount * q_next * (1 - terminals)
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q_next.add_(rewards)
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q_next = q_next.detach()
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q = critic.predict(states, actions)
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critic_loss = self.criterion(q, q_next)
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critic.zero_grad()
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self.critic_opt.zero_grad()
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critic_loss.backward()
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self.critic_opt.step()
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actions = actor.predict(states, False)
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var_actions = Variable(actions.data, requires_grad=True)
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q = critic.predict(states, var_actions)
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q.backward(critic.FloatTensor(np.ones(q.size())))
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actor.zero_grad()
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self.actor_opt.zero_grad()
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actions.backward(-var_actions.grad.data)
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self.actor_opt.step()
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self.soft_update(self.target_network, self.worker_network)
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return total_reward, steps
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