115 lines
5.1 KiB
Python
115 lines
5.1 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: 2021-03-24 22:18:18
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LastEditor: John
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LastEditTime: 2021-03-31 14:51:09
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Discription:
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Environment:
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'''
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import torch
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import torch.nn as nn
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import numpy as np
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import random,math
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import torch.optim as optim
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from common.model import MLP
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from common.memory import ReplayBuffer
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class HierarchicalDQN:
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def __init__(self,state_dim,action_dim,cfg):
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self.state_dim = state_dim
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self.action_dim = action_dim
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self.gamma = cfg.gamma
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self.device = cfg.device
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self.batch_size = cfg.batch_size
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self.frame_idx = 0
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self.epsilon = lambda frame_idx: cfg.epsilon_end + (cfg.epsilon_start - cfg.epsilon_end ) * math.exp(-1. * frame_idx / cfg.epsilon_decay)
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self.policy_net = MLP(2*state_dim, action_dim,cfg.hidden_dim).to(self.device)
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self.meta_policy_net = MLP(state_dim, state_dim,cfg.hidden_dim).to(self.device)
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self.optimizer = optim.Adam(self.policy_net.parameters(),lr=cfg.lr)
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self.meta_optimizer = optim.Adam(self.meta_policy_net.parameters(),lr=cfg.lr)
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self.memory = ReplayBuffer(cfg.memory_capacity)
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self.meta_memory = ReplayBuffer(cfg.memory_capacity)
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self.loss_numpy = 0
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self.meta_loss_numpy = 0
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self.losses = []
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self.meta_losses = []
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def to_onehot(self,x):
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oh = np.zeros(self.state_dim)
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oh[x - 1] = 1.
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return oh
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def set_goal(self,state):
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if random.random() > self.epsilon(self.frame_idx):
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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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goal = self.meta_policy_net(state).max(1)[1].item()
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else:
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goal = random.randrange(self.state_dim)
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return goal
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def choose_action(self,state):
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self.frame_idx += 1
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if random.random() > self.epsilon(self.frame_idx):
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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.action_dim)
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return action
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def update(self):
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self.update_policy()
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self.update_meta()
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def update_policy(self):
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if self.batch_size > len(self.memory):
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return
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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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state_batch = torch.tensor(state_batch,dtype=torch.float)
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action_batch = torch.tensor(action_batch,dtype=torch.int64).unsqueeze(1)
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reward_batch = torch.tensor(reward_batch,dtype=torch.float)
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next_state_batch = torch.tensor(next_state_batch, dtype=torch.float)
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done_batch = torch.tensor(np.float32(done_batch))
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q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch).squeeze(1)
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next_state_values = self.policy_net(next_state_batch).max(1)[0].detach()
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expected_q_values = reward_batch + 0.99 * next_state_values * (1-done_batch)
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loss = nn.MSELoss()(q_values, expected_q_values)
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self.optimizer.zero_grad()
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loss.backward()
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for param in self.policy_net.parameters(): # clip防止梯度爆炸
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param.grad.data.clamp_(-1, 1)
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self.optimizer.step()
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self.loss_numpy = loss.detach().numpy()
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self.losses.append(self.loss_numpy)
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def update_meta(self):
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if self.batch_size > len(self.meta_memory):
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return
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state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.meta_memory.sample(self.batch_size)
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state_batch = torch.tensor(state_batch,dtype=torch.float)
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action_batch = torch.tensor(action_batch,dtype=torch.int64).unsqueeze(1)
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reward_batch = torch.tensor(reward_batch,dtype=torch.float)
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next_state_batch = torch.tensor(next_state_batch, dtype=torch.float)
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done_batch = torch.tensor(np.float32(done_batch))
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q_values = self.meta_policy_net(state_batch).gather(dim=1, index=action_batch).squeeze(1)
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next_state_values = self.meta_policy_net(next_state_batch).max(1)[0].detach()
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expected_q_values = reward_batch + 0.99 * next_state_values * (1-done_batch)
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meta_loss = nn.MSELoss()(q_values, expected_q_values)
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self.meta_optimizer.zero_grad()
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meta_loss.backward()
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for param in self.meta_policy_net.parameters(): # clip防止梯度爆炸
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param.grad.data.clamp_(-1, 1)
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self.meta_optimizer.step()
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self.meta_loss_numpy = meta_loss.detach().numpy()
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self.meta_losses.append(self.meta_loss_numpy)
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def save(self, path):
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torch.save(self.policy_net.state_dict(), path+'policy_checkpoint.pth')
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torch.save(self.meta_policy_net.state_dict(), path+'meta_checkpoint.pth')
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def load(self, path):
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self.policy_net.load_state_dict(torch.load(path+'policy_checkpoint.pth'))
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self.meta_policy_net.load_state_dict(torch.load(path+'meta_checkpoint.pth'))
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