mirror of
https://github.com/wassname/pytorch-soft-actor-critic.git
synced 2026-06-27 18:06:10 +08:00
wassname
95 lines
4.2 KiB
Python
95 lines
4.2 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 tqdm.auto import tqdm
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import apple_gym.env
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import pickle
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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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parser.add_argument('--load-actor', type=str, help='e.g. models/actor_2021-01-02_10-26-23_SAC_ApplePick-v0_Gaussian_autotune.pkl')
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parser.add_argument('--load-critic', type=str, help='e.g. models/critic_2021-01-02_10-26-23_SAC_ApplePick-v0_Gaussian_autotune.pkl')
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args = parser.parse_args()
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torch.manual_seed(args.seed)
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np.random.seed(args.seed)
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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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# Agent
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agent = SAC(env.observation_space.shape[0], env.action_space, args)
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agent.load_model(args.load_actor, args.load_critic)
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# Test
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avg_reward = 0.
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episodes = 10
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for _ in tqdm(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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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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