train_gail.py

""""""

import numpy as np

from openrl.configs.config import create_config_parser
from openrl.envs.common import make
from openrl.envs.wrappers.extra_wrappers import ZeroRewardWrapper
from openrl.modules.common import GAILNet as Net
from openrl.runners.common import GAILAgent as Agent


def train():
    # create the neural network
    cfg_parser = create_config_parser()
    cfg = cfg_parser.parse_args()

    # We use ZeroRewardWrapper to make sure that we don't get any reward from the environment.
    # create environment, set environment parallelism to 9
    env = make("CartPole-v1", env_num=3, cfg=cfg, env_wrappers=[ZeroRewardWrapper])

    net = Net(
        env,
        cfg=cfg,
    )
    # initialize the trainer
    agent = Agent(net)
    # start training, set total number of training steps to 5000
    agent.train(total_time_steps=7500)

    env.close()
    return agent


def evaluation(agent):
    # begin to test
    # Create an environment for testing and set the number of environments to interact with to 9. Set rendering mode to group_human.
    render_mode = (  # use this if you want to see the rendering of the environment
        "group_human"
    )
    render_mode = None
    env = make("CartPole-v1", render_mode=render_mode, env_num=9, asynchronous=True)

    # The trained agent sets up the interactive environment it needs.
    agent.set_env(env)
    # Initialize the environment and get initial observations and environmental information.
    obs, info = env.reset()
    done = False
    step = 0
    while not np.any(done):
        # Based on environmental observation input, predict next action.
        action, _ = agent.act(obs, deterministic=True)
        obs, r, done, info = env.step(action)
        step += 1
        if step % 50 == 0:
            print(f"{step}: reward:{np.mean(r)}")
    env.close()


if __name__ == "__main__":
    agent = train()
    evaluation(agent)