main.py

"""
Run your desired environment and agent configuration.
"""

import os
import atexit
import gym
from agents import QAgent

import environment_wrapper as ew

def run(conf):

    # 1. Create a learning agent
    marvin = conf.agent

    # (2.) *optional* Load agent memory and/or net from disk.
    if conf.load_ann or conf.load_mem:
        marvin.load(conf.save_to + conf.load_from + '/' + conf.load_from, net=conf.load_ann, memory=conf.load_mem)

    # 3. Set your configurations for the run.
    # Register an *atexit* callback,
    # to store the corrent result of the agent
    # if the program is interrupted.
    if conf.learn:
        atexit.register(marvin.save, conf.save_to)

    # Offline training of the agent with
    # previous collected and saved memories.
    if conf.learn_offline and conf.learn:
        ew.learn_offline(marvin, conf)

    # Run the agent in the environment for the
    # number of specified epochs. Either to
    # verify the performance of the agent or
    # to train the agent.
    _LEARN = conf.learn_online and conf.learn
    loss, avg_score = ew.run(conf.env, marvin, conf.run_episodes, render=conf.render, learn=_LEARN, conf=conf)

    # Save the final training result of the agent.
    if conf.learn:
        marvin.save(conf.save_to)
    
    ew.process_logs(avg_score, loss, conf)

    if conf.env_type == 'Carla':
        conf.env.world.destroy()

if __name__ == '__main__':
    conf = ew.Config()
    conf.render = True
    conf.env = gym.make('LunarLander-v2')
    conf.env_type = 'Lunar'
    conf.conf_to_name()
    conf.agent = QAgent(conf)
    run(conf)