update codes

codes/HierarchicalDQN/README.md

@@ -1,13 +0,0 @@
-# Hierarchical DQN
-
-## 原理简介
-
-Hierarchical DQN是一种分层强化学习方法，与DQN相比增加了一个meta controller，
-
-![image-20210331153115575](assets/image-20210331153115575.png)
-
-即学习时，meta controller每次会生成一个goal，然后controller或者说下面的actor就会达到这个goal，直到done为止。这就相当于给agent增加了一个队长，队长擅长制定局部目标，指导agent前行，这样应对一些每回合步数较长或者稀疏奖励的问题会有所帮助。
-
-## 伪代码
-
-![image-20210331153542314](assets/image-20210331153542314.png)

codes/HierarchicalDQN/agent.py

@@ -1,115 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2021-03-24 22:18:18
-LastEditor: John
-LastEditTime: 2021-05-04 22:39:34
-Discription: 
-Environment: 
-'''
-import torch
-import torch.nn as nn
-import numpy as np
-import random,math
-import torch.optim as optim
-from common.model import MLP
-from common.memory import ReplayBuffer
-
-class HierarchicalDQN:
-    def __init__(self,state_dim,action_dim,cfg):
-        self.state_dim = state_dim
-        self.action_dim = action_dim
-        self.gamma = cfg.gamma
-        self.device = cfg.device
-        self.batch_size = cfg.batch_size
-        self.frame_idx = 0 
-        self.epsilon = lambda frame_idx: cfg.epsilon_end + (cfg.epsilon_start - cfg.epsilon_end ) * math.exp(-1. * frame_idx / cfg.epsilon_decay)
-        self.policy_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.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)
-        self.loss_numpy  = 0
-        self.meta_loss_numpy  = 0
-        self.losses = []
-        self.meta_losses = []
-    def to_onehot(self,x):
-        oh = np.zeros(self.state_dim)
-        oh[x - 1] = 1.
-        return oh
-    def set_goal(self,state):
-        if random.random() > self.epsilon(self.frame_idx):
-            with torch.no_grad():
-                state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(0)
-                goal = self.meta_policy_net(state).max(1)[1].item() 
-        else:
-            goal = random.randrange(self.state_dim)
-        return goal
-    def choose_action(self,state):
-        self.frame_idx += 1
-        if random.random() > self.epsilon(self.frame_idx):
-            with torch.no_grad():
-                state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(0)
-                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):
-        self.update_policy()
-        self.update_meta()
-    def update_policy(self): 
-        if self.batch_size > len(self.memory):
-            return
-        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,dtype=torch.int64).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)
-        q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch).squeeze(1)
-        next_state_values = self.policy_net(next_state_batch).max(1)[0].detach()
-        expected_q_values = reward_batch + 0.99 * next_state_values * (1-done_batch)
-        loss = nn.MSELoss()(q_values, expected_q_values) 
-        self.optimizer.zero_grad() 
-        loss.backward()
-        for param in self.policy_net.parameters():  # clip防止梯度爆炸
-            param.grad.data.clamp_(-1, 1)
-        self.optimizer.step()  
-        self.loss_numpy = loss.detach().cpu().numpy()
-        self.losses.append(self.loss_numpy)  
-    def update_meta(self):
-        if self.batch_size > len(self.meta_memory):
-            return
-        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.meta_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,dtype=torch.int64).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)
-        q_values = self.meta_policy_net(state_batch).gather(dim=1, index=action_batch).squeeze(1)
-        next_state_values = self.meta_policy_net(next_state_batch).max(1)[0].detach()
-        expected_q_values = reward_batch + 0.99 * next_state_values * (1-done_batch)
-        meta_loss = nn.MSELoss()(q_values, expected_q_values) 
-        self.meta_optimizer.zero_grad() 
-        meta_loss.backward()
-        for param in self.meta_policy_net.parameters():  # clip防止梯度爆炸
-            param.grad.data.clamp_(-1, 1)
-        self.meta_optimizer.step() 
-        self.meta_loss_numpy = meta_loss.detach().cpu().numpy()
-        self.meta_losses.append(self.meta_loss_numpy)
-
-    def save(self, path):
-        torch.save(self.policy_net.state_dict(), path+'policy_checkpoint.pth')
-        torch.save(self.meta_policy_net.state_dict(), path+'meta_checkpoint.pth')
-
-    def load(self, path):
-        self.policy_net.load_state_dict(torch.load(path+'policy_checkpoint.pth'))
-        self.meta_policy_net.load_state_dict(torch.load(path+'meta_checkpoint.pth'))
-        
-
-        
-        

codes/HierarchicalDQN/task0_train.py

@@ -1,146 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2021-03-29 10:37:32
-LastEditor: John
-LastEditTime: 2021-05-04 22:35:56
-Discription: 
-Environment: 
-'''
-
-
-import sys,os
-curr_path = os.path.dirname(__file__)
-parent_path = os.path.dirname(curr_path)
-sys.path.append(parent_path)  # add current terminal path to sys.path
-
-import datetime
-import numpy as np
-import torch
-import gym
-
-from common.utils import save_results,make_dir
-from common.plot import plot_rewards
-from HierarchicalDQN.agent import HierarchicalDQN
-
-curr_time = datetime.datetime.now().strftime(
-    "%Y%m%d-%H%M%S")  # obtain current time
-
-class HierarchicalDQNConfig:
-    def __init__(self):
-        self.algo = "H-DQN"  # name of algo
-        self.env = 'CartPole-v0'
-        self.result_path = curr_path+"/outputs/" + self.env + \
-            '/'+curr_time+'/results/'  # path to save results
-        self.model_path = curr_path+"/outputs/" + self.env + \
-            '/'+curr_time+'/models/'  # path to save models
-        self.train_eps = 300  # 训练的episode数目
-        self.eval_eps = 50  # 测试的episode数目
-        self.gamma = 0.99
-        self.epsilon_start = 1  # start epsilon of e-greedy policy
-        self.epsilon_end = 0.01
-        self.epsilon_decay = 200
-        self.lr = 0.0001  # learning rate
-        self.memory_capacity = 10000  # Replay Memory capacity
-        self.batch_size = 32
-        self.target_update = 2  # target net的更新频率
-        self.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")  # 检测gpu
-        self.hidden_dim = 256  # dimension of hidden layer
-
-def env_agent_config(cfg,seed=1):
-    env = gym.make(cfg.env)  
-    env.seed(seed)
-    state_dim = env.observation_space.shape[0]
-    action_dim = env.action_space.n
-    agent = HierarchicalDQN(state_dim,action_dim,cfg)
-    return env,agent
-
-def train(cfg, env, agent):
-    print('Start to train !')
-    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
-    rewards = []
-    ma_rewards = []  # moveing average reward
-    for i_ep in range(cfg.train_eps):
-        state = env.reset()
-        done = False
-        ep_reward = 0
-        while not done:
-            goal = agent.set_goal(state)
-            onehot_goal = agent.to_onehot(goal)
-            meta_state = state
-            extrinsic_reward = 0
-            while not done and goal != np.argmax(state):
-                goal_state = np.concatenate([state, onehot_goal])
-                action = agent.choose_action(goal_state)
-                next_state, reward, done, _ = env.step(action)
-                ep_reward += reward
-                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, onehot_goal]), done)
-                state = next_state
-                agent.update()
-        agent.meta_memory.push(meta_state, goal, extrinsic_reward, state, done)
-        print('Episode:{}/{}, Reward:{}, Loss:{:.2f}, Meta_Loss:{:.2f}'.format(i_ep+1, cfg.train_eps, ep_reward,agent.loss_numpy ,agent.meta_loss_numpy ))
-        rewards.append(ep_reward)
-        if ma_rewards:
-            ma_rewards.append(
-                0.9*ma_rewards[-1]+0.1*ep_reward)
-        else:
-            ma_rewards.append(ep_reward)
-    print('Complete training！')
-    return rewards, ma_rewards
-
-def eval(cfg, env, agent):
-    print('Start to eval !')
-    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
-    rewards = []
-    ma_rewards = []  # moveing average reward
-    for i_ep in range(cfg.train_eps):
-        state = env.reset()
-        done = False
-        ep_reward = 0
-        while not done:
-            goal = agent.set_goal(state)
-            onehot_goal = agent.to_onehot(goal)
-            extrinsic_reward = 0
-            while not done and goal != np.argmax(state):
-                goal_state = np.concatenate([state, onehot_goal])
-                action = agent.choose_action(goal_state)
-                next_state, reward, done, _ = env.step(action)
-                ep_reward += reward
-                extrinsic_reward += reward
-                state = next_state
-                agent.update()
-        print(f'Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward}, Loss:{agent.loss_numpy:.2f}, Meta_Loss:{agent.meta_loss_numpy:.2f}')
-        rewards.append(ep_reward)
-        if ma_rewards:
-            ma_rewards.append(
-                0.9*ma_rewards[-1]+0.1*ep_reward)
-        else:
-            ma_rewards.append(ep_reward)
-    print('Complete training！')
-    return rewards, ma_rewards
-
-if __name__ == "__main__":
-    cfg = HierarchicalDQNConfig()
-
-    # train
-    env,agent = env_agent_config(cfg,seed=1)
-    rewards, ma_rewards = train(cfg, env, agent)
-    make_dir(cfg.result_path, cfg.model_path)
-    agent.save(path=cfg.model_path)
-    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
-    plot_rewards(rewards, ma_rewards, tag="train",
-                 algo=cfg.algo, path=cfg.result_path)
-    # eval
-    env,agent = env_agent_config(cfg,seed=10)
-    agent.load(path=cfg.model_path)
-    rewards,ma_rewards = eval(cfg,env,agent)
-    save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
-    plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
-