102 lines
5.1 KiB
Python
102 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: 2021-03-27 04:24:30
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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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from HierarchicalDQN.model import MLP
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from common.memory import ReplayBuffer
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import torch.optim as optim
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class HierarchicalDQN:
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def __init__(self,state_dim,action_dim,cfg):
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self.action_dim = action_dim
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self.device = cfg.device
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self.batch_size = cfg.batch_size
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self.sample_count = 0
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self.epsilon = 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 = MLP(2*state_dim, action_dim,cfg.hidden_dim).to(self.device)
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self.target_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.meta_target_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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def to_onehot(x):
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oh = np.zeros(6)
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oh[x - 1] = 1.
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return oh
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def set_goal(self,meta_state):
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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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self.sample_count += 1
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if random.random() > self.epsilon:
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with torch.no_grad():
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meta_state = torch.tensor([meta_state], device=self.device, dtype=torch.float32)
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q_value = self.policy_net(meta_state)
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goal = q_value.max(1)[1].item()
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else:
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goal = random.randrange(self.action_dim)
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goal = self.meta_policy_net(meta_state)
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onehot_goal = self.to_onehot(goal)
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return onehot_goal
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def choose_action(self,state):
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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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self.sample_count += 1
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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)
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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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if self.batch_size > len(self.memory):
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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(
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state_batch, device=self.device, dtype=torch.float)
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action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1)
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reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float)
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next_state_batch = torch.tensor(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)
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q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch)
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next_state_values = self.target_net(next_state_batch).max(1)[0].detach()
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expected_q_values = reward_batch + self.gamma * next_state_values * (1-done_batch[0])
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loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1))
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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():
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param.grad.data.clamp_(-1, 1)
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self.optimizer.step()
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if self.batch_size > len(self.meta_memory):
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meta_state_batch, meta_action_batch, meta_reward_batch, next_meta_state_batch, meta_done_batch = self.memory.sample(self.batch_size)
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meta_state_batch = torch.tensor(meta_state_batch, device=self.device, dtype=torch.float)
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meta_action_batch = torch.tensor(meta_action_batch, device=self.device).unsqueeze(1)
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meta_reward_batch = torch.tensor(meta_reward_batch, device=self.device, dtype=torch.float)
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next_meta_state_batch = torch.tensor(next_meta_state_batch, device=self.device, dtype=torch.float)
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meta_done_batch = torch.tensor(np.float32(meta_done_batch), device=self.device).unsqueeze(1)
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meta_q_values = self.meta_policy_net(meta_state_batch).gather(dim=1, index=meta_action_batch)
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next_state_values = self.target_net(next_meta_state_batch).max(1)[0].detach()
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expected_meta_q_values = meta_reward_batch + self.gamma * next_state_values * (1-meta_done_batch[0])
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meta_loss = nn.MSEmeta_loss()(meta_q_values, expected_meta_q_values.unsqueeze(1))
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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():
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param.grad.data.clamp_(-1, 1)
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self.meta_optimizer.step()
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