diff --git a/codes/DDPG/README.md b/codes/DDPG/README.md new file mode 100644 index 0000000..351615b --- /dev/null +++ b/codes/DDPG/README.md @@ -0,0 +1,5 @@ +# DDPG + +## 伪代码 + +![image-20210320151900695](assets/image-20210320151900695.png) \ No newline at end of file diff --git a/codes/DDPG/agent.py b/codes/DDPG/agent.py new file mode 100644 index 0000000..29f34d6 --- /dev/null +++ b/codes/DDPG/agent.py @@ -0,0 +1,93 @@ +#!/usr/bin/env python +# coding=utf-8 +''' +@Author: John +@Email: johnjim0816@gmail.com +@Date: 2020-06-09 20:25:52 +@LastEditor: John +LastEditTime: 2021-03-17 20:43:25 +@Discription: +@Environment: python 3.7.7 +''' +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim + +from common.model import Actor, Critic +from common.memory import ReplayBuffer + + +class DDPG: + def __init__(self, n_states, n_actions, cfg): + self.device = cfg.device + self.critic = Critic(n_states, n_actions, cfg.hidden_dim).to(cfg.device) + self.actor = Actor(n_states, n_actions, cfg.hidden_dim).to(cfg.device) + self.target_critic = Critic(n_states, n_actions, cfg.hidden_dim).to(cfg.device) + self.target_actor = Actor(n_states, n_actions, cfg.hidden_dim).to(cfg.device) + + for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()): + target_param.data.copy_(param.data) + for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()): + target_param.data.copy_(param.data) + + self.critic_optimizer = optim.Adam( + self.critic.parameters(), lr=cfg.critic_lr) + self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=cfg.actor_lr) + self.memory = ReplayBuffer(cfg.memory_capacity) + self.batch_size = cfg.batch_size + self.soft_tau = cfg.soft_tau + self.gamma = cfg.gamma + + def choose_action(self, state): + state = torch.FloatTensor(state).unsqueeze(0).to(self.device) + action = self.actor(state) + # torch.detach()用于切断反向传播 + return action.detach().cpu().numpy()[0, 0] + + def update(self): + if len(self.memory) < self.batch_size: + return + state, action, reward, next_state, done = self.memory.sample( + self.batch_size) + # 将所有变量转为张量 + state = torch.FloatTensor(state).to(self.device) + next_state = torch.FloatTensor(next_state).to(self.device) + action = torch.FloatTensor(action).to(self.device) + reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device) + done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device) + # 注意critic将(s_t,a)作为输入 + policy_loss = self.critic(state, self.actor(state)) + + policy_loss = -policy_loss.mean() + + next_action = self.target_actor(next_state) + target_value = self.target_critic(next_state, next_action.detach()) + expected_value = reward + (1.0 - done) * self.gamma * target_value + expected_value = torch.clamp(expected_value, -np.inf, np.inf) + + value = self.critic(state, action) + value_loss = nn.MSELoss()(value, expected_value.detach()) + + self.actor_optimizer.zero_grad() + policy_loss.backward() + self.actor_optimizer.step() + + self.critic_optimizer.zero_grad() + value_loss.backward() + self.critic_optimizer.step() + for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()): + target_param.data.copy_( + target_param.data * (1.0 - self.soft_tau) + + param.data * self.soft_tau + ) + for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()): + target_param.data.copy_( + target_param.data * (1.0 - self.soft_tau) + + param.data * self.soft_tau + ) + def save(self,path): + torch.save(self.target_net.state_dict(), path+'DDPG_checkpoint.pth') + + def load(self,path): + self.actor.load_state_dict(torch.load(path+'DDPG_checkpoint.pth')) \ No newline at end of file diff --git a/codes/DDPG/assets/image-20210320151900695.png b/codes/DDPG/assets/image-20210320151900695.png new file mode 100644 index 0000000..fd41201 Binary files /dev/null and b/codes/DDPG/assets/image-20210320151900695.png differ diff --git a/codes/DDPG/env.py b/codes/DDPG/env.py new file mode 100644 index 0000000..ad7bd0e --- /dev/null +++ b/codes/DDPG/env.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python +# coding=utf-8 +''' +@Author: John +@Email: johnjim0816@gmail.com +@Date: 2020-06-10 15:28:30 +@LastEditor: John +LastEditTime: 2021-03-19 19:56:46 +@Discription: +@Environment: python 3.7.7 +''' +import gym +import numpy as np + +class NormalizedActions(gym.ActionWrapper): + ''' 将action范围重定在[0.1]之间 + ''' + def action(self, action): + + low_bound = self.action_space.low + upper_bound = self.action_space.high + action = low_bound + (action + 1.0) * 0.5 * (upper_bound - low_bound) + action = np.clip(action, low_bound, upper_bound) + + return action + + def reverse_action(self, action): + low_bound = self.action_space.low + upper_bound = self.action_space.high + action = 2 * (action - low_bound) / (upper_bound - low_bound) - 1 + action = np.clip(action, low_bound, upper_bound) + return action + +class OUNoise(object): + '''Ornstein–Uhlenbeck + ''' + def __init__(self, action_space, mu=0.0, theta=0.15, max_sigma=0.3, min_sigma=0.3, decay_period=100000): + self.mu = mu + self.theta = theta + self.sigma = max_sigma + self.max_sigma = max_sigma + self.min_sigma = min_sigma + self.decay_period = decay_period + self.n_actions = action_space.shape[0] + self.low = action_space.low + self.high = action_space.high + self.reset() + + def reset(self): + self.obs = np.ones(self.n_actions) * self.mu + + def evolve_obs(self): + x = self.obs + dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(self.n_actions) + self.obs = x + dx + return self.obs + + def get_action(self, action, t=0): + ou_obs = self.evolve_obs() + self.sigma = self.max_sigma - (self.max_sigma - self.min_sigma) * min(1.0, t / self.decay_period) + return np.clip(action + ou_obs, self.low, self.high) \ No newline at end of file diff --git a/codes/DDPG/main.py b/codes/DDPG/main.py new file mode 100644 index 0000000..5308ec6 --- /dev/null +++ b/codes/DDPG/main.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python +# coding=utf-8 +''' +@Author: John +@Email: johnjim0816@gmail.com +@Date: 2020-06-11 20:58:21 +@LastEditor: John +LastEditTime: 2021-03-19 19:57:00 +@Discription: +@Environment: python 3.7.7 +''' +import sys,os +sys.path.append(os.getcwd()) # 添加当前终端路径 +import torch +import gym +import numpy as np +import datetime +from DDPG.agent import DDPG +from DDPG.env import NormalizedActions,OUNoise +from common.plot import plot_rewards +from common.utils import save_results + +SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间 +SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径 +if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): # 检测是否存在文件夹 + os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/") +if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹 + os.mkdir(SAVED_MODEL_PATH) +RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径 +if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): # 检测是否存在文件夹 + os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/") +if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹 + os.mkdir(RESULT_PATH) + +class DDPGConfig: + def __init__(self): + self.gamma = 0.99 + self.critic_lr = 1e-3 + self.actor_lr = 1e-4 + self.memory_capacity = 10000 + self.batch_size = 128 + self.train_eps =300 + self.train_steps = 200 + self.eval_eps = 200 + self.eval_steps = 200 + self.target_update = 4 + self.hidden_dim = 30 + self.soft_tau=1e-2 + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +def train(cfg,env,agent): + print('Start to train ! ') + ou_noise = OUNoise(env.action_space) # action noise + rewards = [] + ma_rewards = [] # moving average rewards + ep_steps = [] + for i_episode in range(cfg.train_eps): + state = env.reset() + ou_noise.reset() + ep_reward = 0 + for i_step in range(cfg.train_steps): + action = agent.choose_action(state) + action = ou_noise.get_action( + action, i_step) # 即paper中的random process + next_state, reward, done, _ = env.step(action) + ep_reward += reward + agent.memory.push(state, action, reward, next_state, done) + agent.update() + state = next_state + if done: + break + print('Episode:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step+1,done)) + ep_steps.append(i_step) + 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 = DDPGConfig() + env = NormalizedActions(gym.make("Pendulum-v0")) + env.seed(1) # 设置env随机种子 + n_states = env.observation_space.shape[0] + n_actions = env.action_space.shape[0] + agent = DDPG(n_states,n_actions,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) + \ No newline at end of file