train_submission_code.py

import json
import select
import time
import logging
import os

import numpy as np
import aicrowd_helper
import gym
import minerl
from utility.parser import Parser

import coloredlogs
coloredlogs.install(logging.DEBUG)

# --- NOTE ---
# This code is only used for "Research" track submissions
# ------------

# All research-tracks evaluations will be ran on the MineRLObtainDiamondVectorObf-v0 environment
MINERL_GYM_ENV = os.getenv('MINERL_GYM_ENV', 'MineRLObtainDiamondVectorObf-v0')
# You need to ensure that your submission is trained in under MINERL_TRAINING_MAX_STEPS steps
MINERL_TRAINING_MAX_STEPS = int(os.getenv('MINERL_TRAINING_MAX_STEPS', 8000000))
# You need to ensure that your submission is trained by launching less than MINERL_TRAINING_MAX_INSTANCES instances
MINERL_TRAINING_MAX_INSTANCES = int(os.getenv('MINERL_TRAINING_MAX_INSTANCES', 5))
# You need to ensure that your submission is trained within allowed training time.
# Round 1: Training timeout is 5 minutes
# Round 2: Training timeout is 4 days
MINERL_TRAINING_TIMEOUT = int(os.getenv('MINERL_TRAINING_TIMEOUT_MINUTES', 4 * 24 * 60))
# The dataset is available in data/ directory from repository root.
MINERL_DATA_ROOT = os.getenv('MINERL_DATA_ROOT', 'data/')

# Optional: You can view best effort status of your instances with the help of parser.py
# This will give you current state like number of steps completed, instances launched and so on. Make your you keep a tap on the numbers to avoid breaching any limits.
parser = Parser(
    'performance/',
    allowed_environment=MINERL_GYM_ENV,
    maximum_instances=MINERL_TRAINING_MAX_INSTANCES,
    maximum_steps=MINERL_TRAINING_MAX_STEPS,
    raise_on_error=False,
    no_entry_poll_timeout=600,
    submission_timeout=MINERL_TRAINING_TIMEOUT * 60,
    initial_poll_timeout=600
)


def main():
    """
    This function will be called for training phase.
    """
    # NOTE: There is no training phase in introduction track,
    #       so we just leave this empty.
    return


if __name__ == "__main__":
    main()