main.py

from __future__ import division, print_function
import random
import tensorflow as tf
from agent import Agent
from Environment import *
flags = tf.app.flags

# Model
flags.DEFINE_string('model', 'm1', 'Type of model')
flags.DEFINE_boolean('dueling', False, 'Whether to use dueling deep q-network')
flags.DEFINE_boolean('double_q', False, 'Whether to use double q-learning')

# Environment
flags.DEFINE_string('env_name', 'Breakout-v0', 'The name of gym environment to use')
flags.DEFINE_integer('action_repeat', 4, 'The number of action to be repeated')

# Etc
flags.DEFINE_boolean('use_gpu', True, 'Whether to use gpu or not')
flags.DEFINE_string('gpu_fraction', '1/1', 'idx / # of gpu fraction e.g. 1/3, 2/3, 3/3')
flags.DEFINE_boolean('display', False, 'Whether to do display the game screen or not')
flags.DEFINE_boolean('is_train', True, 'Whether to do training or testing')
flags.DEFINE_integer('random_seed', 123, 'Value of random seed')

FLAGS = flags.FLAGS

# Set random seed
tf.set_random_seed(FLAGS.random_seed)
random.seed(FLAGS.random_seed)

if FLAGS.gpu_fraction == '':
  raise ValueError("--gpu_fraction should be defined")

def calc_gpu_fraction(fraction_string):
  idx, num = fraction_string.split('/')
  idx, num = float(idx), float(num)

  fraction = 1 / (num - idx + 1)
  print(" [*] GPU : %.4f" % fraction)
  return fraction

def main(_):

  up_lanes = [3.5/2,3.5/2 + 3.5,250+3.5/2, 250+3.5+3.5/2, 500+3.5/2, 500+3.5+3.5/2]
  down_lanes = [250-3.5-3.5/2,250-3.5/2,500-3.5-3.5/2,500-3.5/2,750-3.5-3.5/2,750-3.5/2]
  left_lanes = [3.5/2,3.5/2 + 3.5,433+3.5/2, 433+3.5+3.5/2, 866+3.5/2, 866+3.5+3.5/2]
  right_lanes = [433-3.5-3.5/2,433-3.5/2,866-3.5-3.5/2,866-3.5/2,1299-3.5-3.5/2,1299-3.5/2]
  width = 750
  height = 1299
  Env = Environ(down_lanes,up_lanes,left_lanes,right_lanes, width, height)
  Env.new_random_game()
  gpu_options = tf.GPUOptions(
      per_process_gpu_memory_fraction=calc_gpu_fraction(FLAGS.gpu_fraction))
  config = tf.ConfigProto()
  config.gpu_options.allow_growth = True

  with tf.Session(config=config) as sess:
    config = []
    agent = Agent(config, Env, sess)
    #agent.play()
    agent.train()

    #agent.play()

if __name__ == '__main__':
    tf.app.run()