113 lines
5.3 KiB
Python
113 lines
5.3 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: 2022-08-18 14:27:18
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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 random
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import math
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import numpy as np
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class DQN:
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def __init__(self,n_actions,model,memory,cfg):
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self.n_actions = 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 = cfg.epsilon_start
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self.sample_count = 0
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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 = model.to(self.device)
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self.target_net = model.to(self.device)
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## copy parameters from policy net to target 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.load_state_dict(self.policy_net.state_dict()) # or use this to copy parameters
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self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
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self.memory = memory
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self.update_flag = False
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def sample_action(self, state):
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''' sample action with e-greedy policy
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'''
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self.sample_count += 1
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# epsilon must decay(linear,exponential and etc.) for balancing exploration and exploitation
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self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
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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(dim=0)
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q_values = self.policy_net(state)
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action = q_values.max(1)[1].item() # choose action corresponding to the maximum q value
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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(dim=0)
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q_values = self.policy_net(state)
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action = q_values.max(1)[1].item() # choose action corresponding to the maximum q value
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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(
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self.batch_size)
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state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float) # shape(batchsize,n_states)
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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)
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next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float) # shape(batchsize,n_states)
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done_batch = torch.tensor(np.float32(done_batch), device=self.device).unsqueeze(1) # shape(batchsize,1)
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# print(state_batch.shape,action_batch.shape,reward_batch.shape,next_state_batch.shape,done_batch.shape)
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# compute current Q(s_t,a), it is 'y_j' in pseucodes
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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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# print(q_values.requires_grad)
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# compute max(Q(s_t+1,A_t+1)) respects to actions A, next_max_q_value comes from another net and is just regarded as constant for q update formula below, thus should detach to requires_grad=False
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next_max_q_value_batch = self.target_net(next_state_batch).max(1)[0].detach().unsqueeze(1)
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# print(q_values.shape,next_q_values.shape)
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# compute expected q value, for terminal state, done_batch[0]=1, and expected_q_value=rewardcorrespondingly
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expected_q_value_batch = reward_batch + self.gamma * next_max_q_value_batch* (1-done_batch)
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# print(expected_q_value_batch.shape,expected_q_value_batch.requires_grad)
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loss = nn.MSELoss()(q_value_batch, expected_q_value_batch) # shape same to
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# backpropagation
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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(), f"{path}/checkpoint.pt")
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def load_model(self, path):
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self.target_net.load_state_dict(torch.load(f"{path}/checkpoint.pt"))
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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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