mirror of
https://github.com/wassname/pytorch-soft-actor-critic.git
synced 2026-06-27 18:06:10 +08:00
wassname
176 lines
7.5 KiB
Python
176 lines
7.5 KiB
Python
import argparse
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import datetime
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import gym
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import numpy as np
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import itertools
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import torch
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from sac import SAC
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from torch.utils.tensorboard import SummaryWriter
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from replay_memory import ReplayMemory
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from load_demonstrations import load_demonstrations
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import apple_gym.env
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import pickle
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from tqdm.auto import tqdm
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parser = argparse.ArgumentParser(description='PyTorch Soft Actor-Critic Args')
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parser.add_argument('-e', '--env-name', default="ApplePick-v0",
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help='Mujoco Gym environment (default: ApplePick-v0)')
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parser.add_argument('--policy', default="Gaussian",
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help='Policy Type: Gaussian | Deterministic (default: Gaussian)')
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parser.add_argument('--eval', type=bool, default=True,
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help='Evaluates a policy a policy every 10 episode (default: True)')
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parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
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help='discount factor for reward (default: 0.99)')
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parser.add_argument('--tau', type=float, default=0.005, metavar='G',
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help='target smoothing coefficient(τ) (default: 0.005)')
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parser.add_argument('--lr', type=float, default=0.0003, metavar='G',
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help='learning rate (default: 0.0003)')
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parser.add_argument('--alpha', type=float, default=0.2, metavar='G',
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help='Temperature parameter α determines the relative importance of the entropy\
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term against the reward (default: 0.2)')
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parser.add_argument('--automatic_entropy_tuning', type=bool, default=True, metavar='G',
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help='Automaically adjust α (default: True)')
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parser.add_argument('--seed', type=int, default=123456, metavar='N',
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help='random seed (default: 123456)')
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parser.add_argument('--batch_size', type=int, default=256, metavar='N',
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help='batch size (default: 256)')
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parser.add_argument('--num_steps', type=int, default=1000001, metavar='N',
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help='maximum number of steps (default: 1000000)')
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parser.add_argument('--hidden_size', type=int, default=256, metavar='N',
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help='hidden size (default: 256)')
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parser.add_argument('--updates_per_step', type=int, default=1, metavar='N',
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help='model updates per simulator step (default: 1)')
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parser.add_argument('--start_steps', type=int, default=10000, metavar='N',
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help='Steps sampling random actions (default: 10000)')
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parser.add_argument('--target_update_interval', type=int, default=1, metavar='N',
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help='Value target update per no. of updates per step (default: 1)')
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parser.add_argument('--replay_size', type=int, default=1000000, metavar='N',
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help='size of replay buffer (default: 10000000)')
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parser.add_argument('--cuda', action="store_true",
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help='run on CUDA (default: False)')
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parser.add_argument('--demonstrations', default=False,
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help='Load demonstrations from https://github.com/erfanMhi/gym-recording-modified')
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parser.add_argument('-l', '--load', default=False,
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help='Load models')
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parser.add_argument('-r', '--render', action="store_true",
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help='show')
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args = parser.parse_args()
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# Environment
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# env = NormalizedActions(gym.make(args.env_name))
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env = gym.make(args.env_name, render=args.render)
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env.seed(args.seed)
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env.action_space.seed(args.seed)
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torch.manual_seed(args.seed)
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np.random.seed(args.seed)
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# Agent
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agent = SAC(env.observation_space.shape[0], env.action_space, args)
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#Tensorboard
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log_name = '{}_SAC_{}_{}_{}'.format(datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"), args.env_name,
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args.policy, "autotune" if args.automatic_entropy_tuning else "")
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writer = SummaryWriter('runs/' + log_name)
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# Memory
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memory=ReplayMemory(args.replay_size, args.seed)
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if args.demonstrations:
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load_demonstrations(memory, args.demonstrations)
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def save():
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agent.save_model(args.env_name, "", "models/actor_" + log_name+'.pkl', "models/critic_"+log_name+'.pkl')
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memory.save(args.env_name, "", "models/memory_" + log_name +'.pkl')
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def load(log_name):
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agent.load_model("models/actor_" + log_name + '.pkl', "models/critic_" + log_name + '.pkl')
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memory.load("models/memory_" + log_name +'.pkl')
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if args.load:
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load(args.load)
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# Training Loop
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total_numsteps = 0
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updates = 0
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with tqdm(unit='frames') as prog:
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for i_episode in itertools.count(1):
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episode_reward = 0
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episode_steps = 0
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done = False
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state = env.reset()
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while not done:
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if args.start_steps > total_numsteps:
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action = env.action_space.sample() # Sample random action
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else:
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action = agent.select_action(state) # Sample action from policy
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if len(memory) > args.batch_size:
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# Number of updates per step in environment
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for i in range(args.updates_per_step):
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# Update parameters of all the networks
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critic_1_loss, critic_2_loss, policy_loss, ent_loss, alpha = agent.update_parameters(memory, args.batch_size, updates)
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writer.add_scalar('loss/critic_1', critic_1_loss, updates)
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writer.add_scalar('loss/critic_2', critic_2_loss, updates)
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writer.add_scalar('loss/policy', policy_loss, updates)
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writer.add_scalar('loss/entropy_loss', ent_loss, updates)
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writer.add_scalar('entropy_temperature/alpha', alpha, updates)
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updates += 1
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next_state, reward, done, info = env.step(action) # Step
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episode_steps += 1
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total_numsteps += 1
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episode_reward += reward
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prog.update(1)
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prog.desc = f'er={episode_reward/episode_steps:2.2f}'
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# for k, v in info.items():
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# if len(v) == 1:
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# writer.add_scalar('env/'+k, v, episode_steps)
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# Ignore the "done" signal if it comes from hitting the time horizon.
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# (https://github.com/openai/spinningup/blob/master/spinup/algos/sac/sac.py)
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mask = 1 if episode_steps == env._max_episode_steps else float(not done)
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memory.push(state, action, reward, next_state, mask) # Append transition to memory
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state = next_state
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if total_numsteps > args.num_steps:
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break
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writer.add_scalar('reward/train', episode_reward, i_episode)
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print("Episode: {}, total numsteps: {}, episode steps: {}, reward: {}".format(i_episode, total_numsteps, episode_steps, round(episode_reward, 2)))
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if i_episode % 10 == 0 and args.eval is True:
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avg_reward = 0.
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episodes = 10
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for _ in range(episodes):
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state = env.reset()
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episode_reward = 0
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done = False
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while not done:
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action = agent.select_action(state, evaluate=True)
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next_state, reward, done, _ = env.step(action)
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episode_reward += reward
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state = next_state
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avg_reward += episode_reward
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avg_reward /= episodes
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writer.add_scalar('avg_reward/test', avg_reward, i_episode)
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save()
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print("----------------------------------------")
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print("Test Episodes: {}, Avg. Reward: {}".format(episodes, round(avg_reward, 2)))
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print("----------------------------------------")
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env.close()
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save()
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