hot update

projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/params.json

@@ -0,0 +1 @@
+{"algo_name": "SoftQ", "env_name": "CartPole-v0", "train_eps": 200, "test_eps": 20, "max_steps": 200, "gamma": 0.99, "alpha": 4, "lr": 0.0001, "memory_capacity": 50000, "batch_size": 128, "target_update": 2, "device": "cpu", "seed": 10, "result_path": "/Users/jj/Desktop/rl-tutorials/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/", "model_path": "/Users/jj/Desktop/rl-tutorials/codes/SoftQ/outputs/CartPole-v0/20220818-154333/models/", "show_fig": false, "save_fig": true}

projects/codes/SoftQ/outputs/CartPole-v0/20220818-154333/results/testing_results.csv

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projects/codes/SoftQ/softq.py

@@ -0,0 +1,71 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from collections import deque
+import random
+from torch.distributions import Categorical
+import gym
+import numpy as np
+
+class SoftQ:
+    def __init__(self,n_actions,model,memory,cfg):
+        self.memory = memory
+        self.alpha = cfg.alpha
+        self.gamma = cfg.gamma  # discount factor
+        self.batch_size = cfg.batch_size
+        self.device = torch.device(cfg.device) 
+        self.policy_net = model.to(self.device)
+        self.target_net = model.to(self.device)
+        self.target_net.load_state_dict(self.policy_net.state_dict()) # copy parameters
+        self.optimizer = torch.optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
+        self.losses = [] # save losses
+
+    def sample_action(self,state):
+        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
+        with torch.no_grad():
+            q = self.policy_net(state)
+            v = self.alpha * torch.log(torch.sum(torch.exp(q/self.alpha), dim=1, keepdim=True)).squeeze()
+            dist = torch.exp((q-v)/self.alpha)
+            dist = dist / torch.sum(dist)
+            c = Categorical(dist)
+            a = c.sample()
+        return a.item()
+    def predict_action(self,state):
+        state = torch.tensor(np.array(state), device=self.device, dtype=torch.float).unsqueeze(0)
+        with torch.no_grad():
+            q = self.policy_net(state)
+            v = self.alpha * torch.log(torch.sum(torch.exp(q/self.alpha), dim=1, keepdim=True)).squeeze()
+            dist = torch.exp((q-v)/self.alpha)
+            dist = dist / torch.sum(dist)
+            c = Categorical(dist)
+            a = c.sample()
+        return a.item()
+    def update(self):
+        if len(self.memory) < self.batch_size: # when the memory capacity does not meet a batch, the network will not update
+            return
+        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(self.batch_size)
+        state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float) # shape(batchsize,n_states)
+        action_batch = torch.tensor(np.array(action_batch), device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize,1)
+        reward_batch = torch.tensor(np.array(reward_batch), device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize,1)
+        next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float) # shape(batchsize,n_states)
+        done_batch = torch.tensor(np.array(done_batch), device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize,1)
+        # print(state_batch.shape,action_batch.shape,reward_batch.shape,next_state_batch.shape,done_batch.shape)
+        with torch.no_grad():
+            next_q = self.target_net(next_state_batch)
+            next_v = self.alpha * torch.log(torch.sum(torch.exp(next_q/self.alpha), dim=1, keepdim=True))
+            y = reward_batch + (1 - done_batch ) * self.gamma * next_v
+        loss = F.mse_loss(self.policy_net(state_batch).gather(1, action_batch.long()), y)
+        self.losses.append(loss)
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+    def save_model(self, path):
+        from pathlib import Path
+        # create path
+        Path(path).mkdir(parents=True, exist_ok=True)
+        torch.save(self.target_net.state_dict(), path+'checkpoint.pth')
+
+    def load_model(self, path):
+        self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))
+        for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
+            param.data.copy_(target_param.data)

projects/codes/SoftQ/task0.py

@@ -0,0 +1,142 @@
+import sys,os
+curr_path = os.path.dirname(os.path.abspath(__file__))  # current path
+parent_path = os.path.dirname(curr_path)  # parent path 
+sys.path.append(parent_path)  # add path to system path
+
+import argparse
+import datetime
+import gym
+import torch
+import random
+import numpy as np
+import torch.nn as nn
+from common.memories import ReplayBufferQue
+from common.models import MLP
+from common.utils import save_results,all_seed,plot_rewards,save_args
+from softq import SoftQ
+
+def get_args():
+    """ hyperparameters
+    """
+    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # obtain current time
+    parser = argparse.ArgumentParser(description="hyperparameters")      
+    parser.add_argument('--algo_name',default='SoftQ',type=str,help="name of algorithm")
+    parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
+    parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
+    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
+    parser.add_argument('--max_steps',default=200,type=int,help="maximum steps per episode")
+    parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
+    parser.add_argument('--alpha',default=4,type=float,help="alpha")
+    parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
+    parser.add_argument('--memory_capacity',default=50000,type=int,help="memory capacity")
+    parser.add_argument('--batch_size',default=128,type=int)
+    parser.add_argument('--target_update',default=2,type=int)
+    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
+    parser.add_argument('--seed',default=10,type=int,help="seed") 
+    parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
+            '/' + curr_time + '/results/' )
+    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
+            '/' + curr_time + '/models/' ) 
+    parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")  
+    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")           
+    args = parser.parse_args()                          
+    return args
+
+class SoftQNetwork(nn.Module):
+    '''Actually almost same to common.models.MLP
+    '''
+    def __init__(self,input_dim,output_dim):
+        super(SoftQNetwork,self).__init__()
+        self.fc1 = nn.Linear(input_dim, 64)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(64, 256)
+        self.fc3 = nn.Linear(256, output_dim)
+        
+    def forward(self, x):
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+def env_agent_config(cfg):
+    ''' create env and agent
+    '''
+    env = gym.make(cfg.env_name)  # create env
+    if cfg.seed !=0: # set random seed
+        all_seed(env,seed=cfg.seed)
+    n_states = env.observation_space.shape[0]  # state dimension
+    n_actions = env.action_space.n  # action dimension
+    print(f"state dim: {n_states}, action dim: {n_actions}")
+    # model = MLP(n_states,n_actions)
+    model = SoftQNetwork(n_states,n_actions)
+    memory =  ReplayBufferQue(cfg.memory_capacity) # replay buffer
+    agent = SoftQ(n_actions,model,memory,cfg)  # create agent
+    return env, agent
+
+def train(cfg, env, agent):
+    ''' training
+    '''
+    print("start training!")
+    print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
+    rewards = []  # record rewards for all episodes
+    steps = [] # record steps for all episodes, sometimes need
+    for i_ep in range(cfg.train_eps):
+        ep_reward = 0  # reward per episode
+        ep_step = 0
+        state = env.reset()   # reset and obtain initial state
+        while True:
+        # for _ in range(cfg.max_steps):
+            ep_step += 1
+            action = agent.sample_action(state) # sample action
+            next_state, reward, done, _ = env.step(action)  # update env and return transitions
+            agent.memory.push((state, action, reward, next_state, done))  # save transitions
+            state = next_state  # update next state for env
+            agent.update()  # update agent
+            ep_reward += reward  
+            if done:
+                break
+        if (i_ep + 1) % cfg.target_update == 0:  # target net update, target_update means "C" in pseucodes
+            agent.target_net.load_state_dict(agent.policy_net.state_dict())
+        steps.append(ep_step)
+        rewards.append(ep_reward)
+        if (i_ep + 1) % 10 == 0:
+            print(f'Episode: {i_ep+1}/{cfg.train_eps}, Reward: {ep_reward:.2f}')
+    print("finish training!")
+    res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
+    return res_dic
+def test(cfg, env, agent):
+    print("start testing!")
+    print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
+    rewards = []  # record rewards for all episodes
+    for i_ep in range(cfg.test_eps):
+        ep_reward = 0  # reward per episode
+        state = env.reset()  # reset and obtain initial state
+        while True:
+            action = agent.predict_action(state)  # predict action
+            next_state, reward, done, _ = env.step(action)  
+            state = next_state  
+            ep_reward += reward 
+            if done:
+                break
+        rewards.append(ep_reward)
+        print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}')
+    print("finish testing!")
+    env.close()
+    return {'episodes':range(len(rewards)),'rewards':rewards}
+
+if __name__ == "__main__":
+    cfg = get_args()
+    # 训练
+    env, agent = env_agent_config(cfg)
+    res_dic = train(cfg, env, agent)
+    save_args(cfg,path = cfg.result_path) # 保存参数到模型路径上
+    agent.save_model(path = cfg.model_path)  # 保存模型
+    save_results(res_dic, tag = 'train', path = cfg.result_path)  
+    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "train")  
+    # 测试
+    env, agent = env_agent_config(cfg) # 也可以不加，加这一行的是为了避免训练之后环境可能会出现问题，因此新建一个环境用于测试
+    agent.load_model(path = cfg.model_path)  # 导入模型
+    res_dic = test(cfg, env, agent)
+    save_results(res_dic, tag='test',
+                 path = cfg.result_path)  # 保存结果
+    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "test")  # 画出结果