update

codes/HierarchicalDQN/agent.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-24 22:18:18
+LastEditor: John
+LastEditTime: 2021-03-27 04:24:30
+Discription: 
+Environment: 
+'''
+import torch
+import torch.nn as nn
+import numpy as np
+import random,math
+from HierarchicalDQN.model import MLP
+from common.memory import ReplayBuffer
+import torch.optim as optim
+class HierarchicalDQN:
+    def __init__(self,state_dim,action_dim,cfg):
+        self.action_dim = action_dim
+        self.device = cfg.device
+        self.batch_size = cfg.batch_size
+        self.sample_count = 0 
+        self.epsilon = 0
+        self.epsilon_start = cfg.epsilon_start
+        self.epsilon_end = cfg.epsilon_end
+        self.epsilon_decay = cfg.epsilon_decay
+        self.batch_size = cfg.batch_size
+        self.policy_net = MLP(2*state_dim, action_dim,cfg.hidden_dim).to(self.device)
+        self.target_net = MLP(2*state_dim, action_dim,cfg.hidden_dim).to(self.device)
+        self.meta_policy_net  = MLP(state_dim, state_dim,cfg.hidden_dim).to(self.device)
+        self.meta_target_net = MLP(state_dim, state_dim,cfg.hidden_dim).to(self.device)
+        self.optimizer = optim.Adam(self.policy_net.parameters(),lr=cfg.lr)
+        self.meta_optimizer = optim.Adam(self.meta_policy_net.parameters(),lr=cfg.lr)
+        self.memory = ReplayBuffer(cfg.memory_capacity)
+        self.meta_memory = ReplayBuffer(cfg.memory_capacity)
+    def to_onehot(x):
+        oh = np.zeros(6)
+        oh[x - 1] = 1.
+        return oh
+    def set_goal(self,meta_state):
+        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * math.exp(-1. * self.sample_count / self.epsilon_decay)
+        self.sample_count += 1
+        if random.random() > self.epsilon:
+            with torch.no_grad():
+                meta_state = torch.tensor([meta_state], device=self.device, dtype=torch.float32)
+                q_value = self.policy_net(meta_state)
+                goal = q_value.max(1)[1].item() 
+        else:
+            goal = random.randrange(self.action_dim)
+        goal = self.meta_policy_net(meta_state)
+        onehot_goal = self.to_onehot(goal)
+        return onehot_goal
+    def choose_action(self,state):
+        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * math.exp(-1. * self.sample_count / self.epsilon_decay)
+        self.sample_count += 1
+        if random.random() > self.epsilon:
+            with torch.no_grad():
+                state = torch.tensor([state], device=self.device, dtype=torch.float32)
+                q_value = self.policy_net(state)
+                action = q_value.max(1)[1].item()  
+        else:
+            action = random.randrange(self.action_dim)
+        return action
+    def update(self):
+        if self.batch_size > len(self.memory):
+            state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(self.batch_size)
+        state_batch = torch.tensor(
+            state_batch, device=self.device, dtype=torch.float)
+        action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1)  
+        reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float)  
+        next_state_batch = torch.tensor(next_state_batch, device=self.device, dtype=torch.float)
+        done_batch = torch.tensor(np.float32(done_batch), device=self.device).unsqueeze(1)  
+        q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch)
+        next_state_values = self.target_net(next_state_batch).max(1)[0].detach()  
+        expected_q_values = reward_batch + self.gamma * next_state_values * (1-done_batch[0])
+        loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) 
+        self.optimizer.zero_grad() 
+        loss.backward()
+        for param in self.policy_net.parameters(): 
+            param.grad.data.clamp_(-1, 1)
+        self.optimizer.step() 
+
+        if self.batch_size > len(self.meta_memory):
+            meta_state_batch, meta_action_batch, meta_reward_batch, next_meta_state_batch, meta_done_batch = self.memory.sample(self.batch_size)
+        meta_state_batch = torch.tensor(meta_state_batch, device=self.device, dtype=torch.float)
+        meta_action_batch = torch.tensor(meta_action_batch, device=self.device).unsqueeze(1)  
+        meta_reward_batch = torch.tensor(meta_reward_batch, device=self.device, dtype=torch.float)  
+        next_meta_state_batch = torch.tensor(next_meta_state_batch, device=self.device, dtype=torch.float)
+        meta_done_batch = torch.tensor(np.float32(meta_done_batch), device=self.device).unsqueeze(1)  
+        meta_q_values = self.meta_policy_net(meta_state_batch).gather(dim=1, index=meta_action_batch)
+        next_state_values = self.target_net(next_meta_state_batch).max(1)[0].detach()  
+        expected_meta_q_values = meta_reward_batch + self.gamma * next_state_values * (1-meta_done_batch[0])
+        meta_loss = nn.MSEmeta_loss()(meta_q_values, expected_meta_q_values.unsqueeze(1)) 
+        self.meta_optimizer.zero_grad() 
+        meta_loss.backward()
+        for param in self.meta_policy_net.parameters(): 
+            param.grad.data.clamp_(-1, 1)
+        self.meta_optimizer.step() 
+        
+