107 lines
4.9 KiB
Python
107 lines
4.9 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: 2020-06-12 00:50:49
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@LastEditor: John
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LastEditTime: 2022-08-29 23:34:20
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@Discription:
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@Environment: python 3.7.7
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'''
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'''off-policy
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'''
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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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import torch.nn.functional as F
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import random
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import math
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import numpy as np
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class DoubleDQN:
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def __init__(self,models, memories, cfg):
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self.n_actions = cfg['n_actions']
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self.device = torch.device(cfg['device'])
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self.gamma = cfg['gamma']
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## e-greedy parameters
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self.sample_count = 0 # sample count for epsilon decay
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self.epsilon_start = cfg['epsilon_start']
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self.epsilon_end = cfg['epsilon_end']
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self.epsilon_decay = cfg['epsilon_decay']
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self.batch_size = cfg['batch_size']
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self.policy_net = models['Qnet'].to(self.device)
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self.target_net = models['Qnet'].to(self.device)
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# target_net copy from policy_net
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for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
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target_param.data.copy_(param.data)
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# self.target_net.eval() # donnot use BatchNormalization or Dropout
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# the difference between parameters() and state_dict() is that parameters() require_grad=True
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self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg['lr'])
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self.memory = memories['Memory']
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self.update_flag = False
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def sample_action(self, state):
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''' sample action
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'''
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self.sample_count += 1
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self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * math.exp(-1. * self.sample_count / self.epsilon_decay)
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if random.random() > self.epsilon:
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with torch.no_grad():
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state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(0)
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q_value = self.policy_net(state)
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action = q_value.max(1)[1].item()
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else:
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action = random.randrange(self.n_actions)
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return action
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def predict_action(self, state):
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''' predict action
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'''
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with torch.no_grad():
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state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(0)
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q_value = self.policy_net(state)
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action = q_value.max(1)[1].item()
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return action
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def update(self):
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if len(self.memory) < self.batch_size: # when transitions in memory donot meet a batch, not update
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return
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else:
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if not self.update_flag:
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print("Begin to update!")
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self.update_flag = True
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# sample a batch of transitions from replay buffer
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state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(self.batch_size)
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# convert to tensor
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state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float)
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action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1) # shape(batchsize,1)
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reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize,1)
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next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float)
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done_batch = torch.tensor(np.float32(done_batch), device=self.device).unsqueeze(1) # shape(batchsize,1)
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# compute current Q(s_t|a=a_t)
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q_value_batch = self.policy_net(state_batch).gather(dim=1, index=action_batch) # shape(batchsize,1),requires_grad=True
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next_q_value_batch = self.policy_net(next_state_batch)
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'''the following is the way of computing Double DQN expected_q_value，a bit different from Nature DQN'''
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next_target_value_batch = self.target_net(next_state_batch)
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# choose action a from Q(s_t‘, a), next_target_values obtain next_q_value，which is Q’(s_t|a=argmax Q(s_t‘, a))
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next_target_q_value_batch = next_target_value_batch.gather(1, torch.max(next_q_value_batch, 1)[1].unsqueeze(1)) # shape(batchsize,1)
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expected_q_value_batch = reward_batch + self.gamma * next_target_q_value_batch * (1-done_batch)
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loss = nn.MSELoss()(q_value_batch , expected_q_value_batch)
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self.optimizer.zero_grad()
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loss.backward()
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# clip to avoid gradient explosion
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for param in self.policy_net.parameters():
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param.grad.data.clamp_(-1, 1)
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self.optimizer.step()
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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.target_net.state_dict(), path+'checkpoint.pth')
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def load_model(self,path):
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self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))
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for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
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param.data.copy_(target_param.data)
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