main.py

import sys
import os
import gym
import time
import matplotlib.pyplot as plt
from a3c.discrete_A3C import train, evaluate, evaluate_checkpoints
from wordle_env.wordle import WordleEnvBase


def print_results(global_ep, win_ep, res):
    print("Jugadas:", global_ep.value)
    print("Ganadas:", win_ep.value)
    plt.plot(res)
    plt.ylabel('Moving average ep reward')
    plt.xlabel('Step')
    plt.show()


if __name__ == "__main__":
    max_ep = int(sys.argv[1]) if len(sys.argv) > 1 else 100000
    env_id = sys.argv[2] if len(sys.argv) > 2 else 'WordleEnv100FullAction-v0'
    evaluation = True if len(sys.argv) > 3 and sys.argv[3] == 'evaluation' else False
    pretrained = True if len(sys.argv) > 3 and sys.argv[3] == 'pretrained' else False
    env = gym.make(env_id)
    model_checkpoint_dir = os.path.join('checkpoints', env.unwrapped.spec.id)
    if not evaluation:
        start_time = time.time()
        if pretrained:
            pretrained_model_path = os.path.join(model_checkpoint_dir, sys.argv[4]) if len(sys.argv) > 4 else ''
            global_ep, win_ep, gnet, res = train(env, max_ep, model_checkpoint_dir, pretrained_model_path)
        else:
            global_ep, win_ep, gnet, res = train(env, max_ep, model_checkpoint_dir)
        print("--- %.0f seconds ---" % (time.time() - start_time))
        print_results(global_ep, win_ep, res)
        evaluate(gnet, env)
    else:
        print("Evaluation mode")
        results = evaluate_checkpoints(model_checkpoint_dir, env)
        print(results)