train.py

import gym
from utils.preprocess import greyscale
from utils.wrappers import PreproWrapper, MaxAndSkipEnv

from q1_schedule import LinearExploration, LinearSchedule
from q2_linear import Linear

from configs.hanabi import config
from deeplearning import DeepQLearning

"""
Use linear approximation for the Atari game. Please report the final result.
Feel free to change the configurations (in the configs/ folder). 
If so, please report your hyperparameters.

You'll find the results, log and video recordings of your agent every 250k under
the corresponding file in the results folder. A good way to monitor the progress
of the training is to use Tensorboard. The starter code writes summaries of different
variables.

To launch tensorboard, open a Terminal window and run 
tensorboard --logdir=results/
Then, connect remotely to 
address-ip-of-the-server:6006 
6006 is the default port used by tensorboard.
"""
if __name__ == '__main__':
    # make env
    env = DeepQLearning(player_num=5)

    # exploration strategy
    exp_schedule = LinearExploration(env, config.eps_begin, 
            config.eps_end, config.eps_nsteps)

    # learning rate schedule
    lr_schedule  = LinearSchedule(config.lr_begin, config.lr_end,
            config.lr_nsteps)

    # train model
    model = Linear(env, config)
    model.run(exp_schedule, lr_schedule)