174 lines
6.0 KiB
Python
174 lines
6.0 KiB
Python
import sys,os
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curr_path = os.path.dirname(__file__)
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parent_path=os.path.dirname(curr_path)
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sys.path.append(parent_path) # add current terminal path to sys.path
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import torch
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import gym
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import numpy as np
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import datetime
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from TD3.agent import TD3
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from common.plot import plot_rewards
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from common.utils import save_results,make_dir
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
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class TD3Config:
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def __init__(self) -> None:
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self.algo = 'TD3'
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self.env = 'HalfCheetah-v2'
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self.seed = 0
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self.result_path = curr_path+"/results/" +self.env+'/'+curr_time+'/results/' # path to save results
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self.model_path = curr_path+"/results/" +self.env+'/'+curr_time+'/models/' # path to save models
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self.start_timestep = 25e3 # Time steps initial random policy is used
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self.eval_freq = 5e3 # How often (time steps) we evaluate
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# self.train_eps = 800
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self.max_timestep = 4000000 # Max time steps to run environment
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self.expl_noise = 0.1 # Std of Gaussian exploration noise
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self.batch_size = 256 # Batch size for both actor and critic
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self.gamma = 0.99 # gamma factor
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self.lr = 0.0005 # Target network update rate
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self.policy_noise = 0.2 # Noise added to target policy during critic update
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self.noise_clip = 0.5 # Range to clip target policy noise
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self.policy_freq = 2 # Frequency of delayed policy updates
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Runs policy for X episodes and returns average reward
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# A fixed seed is used for the eval environment
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def eval(env,agent, seed, eval_episodes=10):
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eval_env = gym.make(env)
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eval_env.seed(seed + 100)
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avg_reward = 0.
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for _ in range(eval_episodes):
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state, done = eval_env.reset(), False
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while not done:
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# eval_env.render()
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action = agent.choose_action(np.array(state))
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state, reward, done, _ = eval_env.step(action)
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avg_reward += reward
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avg_reward /= eval_episodes
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print("---------------------------------------")
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print(f"Evaluation over {eval_episodes} episodes: {avg_reward:.3f}")
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print("---------------------------------------")
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return avg_reward
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def train(cfg,env,agent):
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# Evaluate untrained policy
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evaluations = [eval(cfg.env,agent, cfg.seed)]
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state, done = env.reset(), False
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ep_reward = 0
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ep_timesteps = 0
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episode_num = 0
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rewards = []
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ma_rewards = [] # moveing average reward
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for t in range(int(cfg.max_timestep)):
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ep_timesteps += 1
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# Select action randomly or according to policy
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if t < cfg.start_timestep:
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action = env.action_space.sample()
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else:
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action = (
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agent.choose_action(np.array(state))
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+ np.random.normal(0, max_action * cfg.expl_noise, size=action_dim)
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).clip(-max_action, max_action)
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# Perform action
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next_state, reward, done, _ = env.step(action)
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done_bool = float(done) if ep_timesteps < env._max_episode_steps else 0
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# Store data in replay buffer
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agent.memory.push(state, action, next_state, reward, done_bool)
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state = next_state
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ep_reward += reward
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# Train agent after collecting sufficient data
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if t >= cfg.start_timestep:
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agent.update()
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if done:
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# +1 to account for 0 indexing. +0 on ep_timesteps since it will increment +1 even if done=True
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print(f"Episode:{episode_num+1}, Episode T:{ep_timesteps}, Reward:{ep_reward:.3f}")
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# Reset environment
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state, done = env.reset(), False
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rewards.append(ep_reward)
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# 计算滑动窗口的reward
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if ma_rewards:
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ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
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else:
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ma_rewards.append(ep_reward)
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ep_reward = 0
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ep_timesteps = 0
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episode_num += 1
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# Evaluate episode
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if (t + 1) % cfg.eval_freq == 0:
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evaluations.append(eval(cfg.env,agent, cfg.seed))
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return rewards, ma_rewards
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# def train(cfg,env,agent):
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# evaluations = [eval(cfg.env,agent,cfg.seed)]
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# ep_reward = 0
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# tot_timestep = 0
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# rewards = []
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# ma_rewards = [] # moveing average reward
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# for i_ep in range(int(cfg.train_eps)):
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# state, done = env.reset(), False
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# ep_reward = 0
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# ep_timestep = 0
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# while not done:
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# ep_timestep += 1
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# tot_timestep +=1
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# # Select action randomly or according to policy
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# if tot_timestep < cfg.start_timestep:
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# action = env.action_space.sample()
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# else:
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# action = (
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# agent.choose_action(np.array(state))
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# + np.random.normal(0, max_action * cfg.expl_noise, size=action_dim)
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# ).clip(-max_action, max_action)
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# # action = (
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# # agent.choose_action(np.array(state))
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# # + np.random.normal(0, max_action * cfg.expl_noise, size=action_dim)
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# # ).clip(-max_action, max_action)
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# # Perform action
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# next_state, reward, done, _ = env.step(action)
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# done_bool = float(done) if ep_timestep < env._max_episode_steps else 0
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# # Store data in replay buffer
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# agent.memory.push(state, action, next_state, reward, done_bool)
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# state = next_state
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# ep_reward += reward
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# # Train agent after collecting sufficient data
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# if tot_timestep >= cfg.start_timestep:
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# agent.update()
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# print(f"Episode:{i_ep}/{cfg.train_eps}, Episode Timestep:{ep_timestep}, Reward:{ep_reward:.3f}")
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# rewards.append(ep_reward)
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# # 计算滑动窗口的reward
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# if ma_rewards:
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# ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
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# else:
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# ma_rewards.append(ep_reward)
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# # Evaluate episode
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# if (i_ep+1) % cfg.eval_freq == 0:
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# evaluations.append(eval(cfg.env,agent, cfg.seed))
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# return rewards,ma_rewards
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if __name__ == "__main__":
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cfg = TD3Config()
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env = gym.make(cfg.env)
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env.seed(cfg.seed) # Set seeds
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torch.manual_seed(cfg.seed)
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np.random.seed(cfg.seed)
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state_dim = env.observation_space.shape[0]
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action_dim = env.action_space.shape[0]
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max_action = float(env.action_space.high[0])
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agent = TD3(state_dim,action_dim,max_action,cfg)
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rewards,ma_rewards = train(cfg,env,agent)
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make_dir(cfg.result_path,cfg.model_path)
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agent.save(path=cfg.model_path)
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save_results(rewards,ma_rewards,tag='train',path=cfg.result_path)
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plot_rewards(rewards,ma_rewards,tag="train",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
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# cfg.result_path = './TD3/results/HalfCheetah-v2/20210416-130341/'
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# agent.load(cfg.result_path)
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# eval(cfg.env,agent, cfg.seed)
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