hot update

projects/codes/DQN/main.py

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+import sys,os
+os.environ["KMP_DUPLICATE_LIB_OK"]  =  "TRUE" # avoid "OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized."
+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 gym
+import torch
+import datetime
+import numpy as np
+import argparse
+from common.utils import save_results,all_seed
+from common.utils import plot_rewards,save_args
+from common.models import MLP
+from common.memories import ReplayBuffer
+from dqn import DQN
+
+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='DQN',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('--gamma',default=0.95,type=float,help="discounted factor")
+    parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
+    parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
+    parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
+    parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
+    parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
+    parser.add_argument('--batch_size',default=64,type=int)
+    parser.add_argument('--target_update',default=4,type=int)
+    parser.add_argument('--hidden_dim',default=256,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('--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")
+    # please manually change the following args in this script if you want
+    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' )    
+    args = parser.parse_args()    
+    args = {**vars(args)}  # type(dict)         
+    return args
+
+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"n_states: {n_states}, n_actions: {n_actions}")
+    cfg.update({"n_states":n_states,"n_actions":n_actions}) # update to cfg paramters
+    model = MLP(n_states,n_actions,hidden_dim=cfg["hidden_dim"])
+    memory =  ReplayBuffer(cfg["memory_capacity"]) # replay buffer
+    agent = DQN(model,memory,cfg)  # create agent
+    return env, agent
+
+def train(cfg, env, agent):
+    ''' 训练
+    '''
+    print("start training!")
+    print(f"Env: {cfg['env_name']}, Algo: {cfg['algo_name']}, Device: {cfg['device']}")
+    rewards = []  # record rewards for all episodes
+    steps = []
+    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:
+            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}: Epislon: {agent.epsilon:.3f}')
+    print("finish training!")
+    env.close()
+    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
+    steps = []
+    for i_ep in range(cfg.test_eps):
+        ep_reward = 0  # reward per episode
+        ep_step = 0
+        state = env.reset()  # reset and obtain initial state
+        while True:
+            ep_step+=1
+            action = agent.predict_action(state)  # predict action
+            next_state, reward, done, _ = env.step(action)  
+            state = next_state  
+            ep_reward += reward 
+            if done:
+                break
+        steps.append(ep_step)
+        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()
+    # training
+    env, agent = env_agent_config(cfg)
+    res_dic = train(cfg, env, agent)
+    save_args(cfg,path = cfg['result_path']) # save parameters
+    agent.save_model(path = cfg['model_path'])  # save models
+    save_results(res_dic, tag = 'train', path = cfg['result_path']) # save results
+    plot_rewards(res_dic['rewards'], cfg, path = cfg['result_path'],tag = "train")  # plot results
+    # testing
+    env, agent = env_agent_config(cfg) # create new env for testing, sometimes can ignore this step
+    agent.load_model(path = cfg['model_path'])  # load model
+    res_dic = test(cfg, env, agent)
+    save_results(res_dic, tag='test',
+                 path = cfg['result_path'])  
+    plot_rewards(res_dic['rewards'], cfg, path = cfg['result_path'],tag = "test")