96 lines
3.9 KiB
Python
96 lines
3.9 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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@Author: John
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@Email: johnjim0816@gmail.com
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@Date: 2020-06-09 20:25:52
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@LastEditor: John
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LastEditTime: 2022-09-27 15:43:21
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@Discription:
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@Environment: python 3.7.7
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'''
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import random
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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class DDPG:
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def __init__(self, models,memories,cfg):
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self.device = torch.device(cfg['device'])
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self.critic = models['critic'].to(self.device)
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self.target_critic = models['critic'].to(self.device)
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self.actor = models['actor'].to(self.device)
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self.target_actor = models['actor'].to(self.device)
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# copy weights from critic to target_critic
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for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
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target_param.data.copy_(param.data)
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# copy weights from actor to target_actor
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for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
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target_param.data.copy_(param.data)
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self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=cfg['critic_lr'])
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self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=cfg['actor_lr'])
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self.memory = memories['memory']
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self.batch_size = cfg['batch_size']
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self.gamma = cfg['gamma']
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self.tau = cfg['tau']
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def sample_action(self, state):
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state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
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action = self.actor(state)
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return action.detach().cpu().numpy()[0, 0]
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@torch.no_grad()
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def predict_action(self, state):
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''' predict action
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'''
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state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
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action = self.actor(state)
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return action.cpu().numpy()[0, 0]
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def update(self):
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if len(self.memory) < self.batch_size: # when memory size is less than batch size, return
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return
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# sample a random minibatch of N transitions from R
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state, action, reward, next_state, done = self.memory.sample(self.batch_size)
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# convert to tensor
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state = torch.FloatTensor(np.array(state)).to(self.device)
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next_state = torch.FloatTensor(np.array(next_state)).to(self.device)
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action = torch.FloatTensor(np.array(action)).to(self.device)
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reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
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done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
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policy_loss = self.critic(state, self.actor(state))
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policy_loss = -policy_loss.mean()
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next_action = self.target_actor(next_state)
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target_value = self.target_critic(next_state, next_action.detach())
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expected_value = reward + (1.0 - done) * self.gamma * target_value
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expected_value = torch.clamp(expected_value, -np.inf, np.inf)
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value = self.critic(state, action)
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value_loss = nn.MSELoss()(value, expected_value.detach())
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self.actor_optimizer.zero_grad()
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policy_loss.backward()
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self.actor_optimizer.step()
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self.critic_optimizer.zero_grad()
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value_loss.backward()
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self.critic_optimizer.step()
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# soft update
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for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
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target_param.data.copy_(
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target_param.data * (1.0 - self.tau) +
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param.data * self.tau
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)
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for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
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target_param.data.copy_(
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target_param.data * (1.0 - self.tau) +
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param.data * self.tau
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)
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def save_model(self,path):
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from pathlib import Path
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# create path
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Path(path).mkdir(parents=True, exist_ok=True)
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torch.save(self.actor.state_dict(), f"{path}/actor_checkpoint.pt")
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def load_model(self,path):
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self.actor.load_state_dict(torch.load(f"{path}/actor_checkpoint.pt")) |