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() 
+        
+        

codes/HierarchicalDQN/main.py

@@ -0,0 +1,97 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-24 22:14:04
+LastEditor: John
+LastEditTime: 2021-03-27 04:23:43
+Discription: 
+Environment: 
+'''
+import sys,os
+sys.path.append(os.getcwd()) # add current terminal path to sys.path
+import gym
+import numpy as np
+import torch
+import datetime
+from HierarchicalDQN.agent import HierarchicalDQN
+from common.plot import plot_rewards
+from common.utils import save_results
+
+SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
+SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'  # path to save model
+if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): 
+    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
+if not os.path.exists(SAVED_MODEL_PATH):
+    os.mkdir(SAVED_MODEL_PATH)
+RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # path to save rewards
+if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): 
+    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
+if not os.path.exists(RESULT_PATH): 
+    os.mkdir(RESULT_PATH)
+
+class HierarchicalDQNConfig:
+    def __init__(self):
+        self.algo = "DQN" # name of algo
+        self.gamma = 0.99
+        self.epsilon_start = 0.95 # start epsilon of e-greedy policy
+        self.epsilon_end = 0.01
+        self.epsilon_decay = 200
+        self.lr = 0.01 # learning rate
+        self.memory_capacity = 800 # Replay Memory capacity
+        self.batch_size = 64
+        self.train_eps = 250 # 训练的episode数目
+        self.train_steps = 200 # 训练每个episode的最大长度
+        self.target_update = 2 # target net的更新频率
+        self.eval_eps = 20 # 测试的episode数目
+        self.eval_steps = 200 # 测试每个episode的最大长度
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
+        self.hidden_dim = 256 # dimension of hidden layer
+
+def train(cfg,env,agent):
+    print('Start to train !')
+    rewards = []
+    ma_rewards = [] # moving average reward
+    ep_steps = []
+    for i_episode in range(cfg.train_eps):
+        state = env.reset() 
+        extrinsic_reward = 0
+        for i_step in range(cfg.train_steps):
+            goal= agent.set_goal(state)
+            meta_state = state
+            goal_state  = np.concatenate([state, goal])
+            action = agent.choose_action(state) 
+            next_state, reward, done, _ = env.step(action)
+            extrinsic_reward += reward
+            intrinsic_reward = 1.0 if goal == np.argmax(next_state) else 0.0
+            agent.memory.push(goal_state, action, intrinsic_reward, np.concatenate([next_state, goal]), done)
+            state = next_state 
+            agent.update()
+            if done:
+                break
+        if i_episode % cfg.target_update == 0:
+            agent.target_net.load_state_dict(agent.policy_net.state_dict())
+        print('Episode:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,extrinsic_reward,i_step+1,done))
+        ep_steps.append(i_step)
+        rewards.append(extrinsic_reward)
+        if ma_rewards:
+            ma_rewards.append(
+                0.9*ma_rewards[-1]+0.1*extrinsic_reward)
+        else:
+            ma_rewards.append(extrinsic_reward)   
+    agent.meta_memory.push(meta_state, goal, extrinsic_reward, state, done)
+    print('Complete training！')
+    return rewards,ma_rewards
+
+if __name__ == "__main__":
+    cfg = HierarchicalDQNConfig()
+    env = gym.make('CartPole-v0')
+    env.seed(1) 
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.n
+    agent = HierarchicalDQN(state_dim,action_dim,cfg)
+    rewards,ma_rewards = train(cfg,env,agent)
+    agent.save(path=SAVED_MODEL_PATH)
+    save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
+    plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)

codes/HierarchicalDQN/model.py

@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-24 22:14:12
+LastEditor: John
+LastEditTime: 2021-03-24 22:17:09
+Discription: 
+Environment: 
+'''
+import torch.nn as nn
+import torch.nn.functional as F
+class MLP(nn.Module):
+    def __init__(self, state_dim,action_dim,hidden_dim=128):
+        super(MLP, self).__init__()
+        self.fc1 = nn.Linear(state_dim, hidden_dim) 
+        self.fc2 = nn.Linear(hidden_dim,hidden_dim)
+        self.fc3 = nn.Linear(hidden_dim, action_dim) 
+        
+    def forward(self, x):
+        x = F.relu(self.fc1(x)) 
+        x = F.relu(self.fc2(x))
+        return self.fc3(x)