import gym
from envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv

from baselines import deepq

import datetime



def callback(lcl, glb):
    # stop training if reward exceeds 199
    total = sum(lcl['episode_rewards'][-101:-1]) / 100
    totalt = lcl['t']
    is_solved = totalt > 2000 and total >= -50
    return is_solved

def main():
  
    env = RacecarZEDGymEnv(renders=False)
    model = deepq.models.cnn_to_mlp(
        convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
        hiddens=[256],
        dueling=False
    )
    act = deepq.learn(
        env,
        q_func=model,
        lr=1e-3,
        max_timesteps=10000,
        buffer_size=50000,
        exploration_fraction=0.1,
        exploration_final_eps=0.02,
        print_freq=10,
        callback=callback
    )
    print("Saving model to racecar_zed_model.pkl")
    act.save("racecar_zed_model.pkl")


if __name__ == '__main__':
    main()