<commit_before>import sys
import os

from absl import flags
from baselines import deepq
from pysc2.env import sc2_env
from pysc2.lib import actions
import os
from baselines import logger
from baselines.common import set_global_seeds

import deepq_mineral_shards
import datetime

from common.vec_env.subproc_vec_env import SubprocVecEnv
from a2c.policies import CnnPolicy
from a2c import a2c
from baselines.logger import Logger, TensorBoardOutputFormat, HumanOutputFormat

import random

import deepq_mineral_4way

import threading
import time

_MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id
_SELECT_ARMY = actions.FUNCTIONS.select_army.id
_SELECT_ALL = [0]
_NOT_QUEUED = [0]

step_mul = 1

FLAGS = flags.FLAGS
flags.DEFINE_string("map", "CollectMineralShards",
                    "Name of a map to use to play.")
start_time = datetime.datetime.now().strftime("%Y%m%d%H%M")
flags.DEFINE_string("log", "tensorboard", "logging type(stdout, tensorboard)")
flags.DEFINE_string("algorithm", "a2c", "RL algorithm to use.")
flags.DEFINE_integer("timesteps", 2000000, "Steps to train")
flags.DEFINE_float("exploration_fraction", 0.5, "Exploration Fraction")
flags.DEFINE_boolean("prioritized", True, "prioritized_replay")
flags.DEFINE_boolean("dueling", True, "dueling")
flags.DEFINE_float("lr", 0.0005, "Learning rate")
flags.DEFINE_integer("num_agents", 4, "number of RL agents for A2C")
flags.DEFINE_integer("num_scripts", 4, "number of script agents for A2C")
flags.DEFINE_integer("nsteps", 20, "number of batch steps for A2C")

PROJ_DIR = os.path.dirname(os.path.abspath(__file__))

max_mean_reward = 0
last_filename = ""

start_time = datetime.datetime.now().strftime("%m%d%H%M")


def main():
  FLAGS(sys.argv)

  print("algorithm : %s" % FLAGS.algorithm)
  print("timesteps : %s" % FLAGS.timesteps)
  print("exploration_fraction : %s" % FLAGS.exploration_fraction)
  print("prioritized : %s" % FLAGS.prioritized)
  print("dueling : %s" % FLAGS.dueling)
  print("num_agents : %s" % FLAGS.num_agents)
  print("lr : %s" % FLAGS.lr)

  if(FLAGS.lr == 0):
    FLAGS.lr = random.uniform(0.00001, 0.001)
    
  print("random lr : %s" % FLAGS.lr)

  lr_round = round(FLAGS.lr, 8)

  logdir = "tensorboard"

  if (FLAGS.algorithm == "deepq-4way"):
    logdir = "tensorboard/mineral/%s/%s_%s_prio%s_duel%s_lr%s/%s" % (
      FLAGS.algorithm, FLAGS.timesteps, FLAGS.exploration_fraction,
      FLAGS.prioritized, FLAGS.dueling, lr_round, start_time)
  elif (FLAGS.algorithm == "deepq"):
    logdir = "tensorboard/mineral/%s/%s_%s_prio%s_duel%s_lr%s/%s" % (
      FLAGS.algorithm, FLAGS.timesteps, FLAGS.exploration_fraction,
      FLAGS.prioritized, FLAGS.dueling, lr_round, start_time)
  elif (FLAGS.algorithm == "a2c"):
    logdir = "tensorboard/mineral/%s/%s_n%s_s%s_nsteps%s/lr%s/%s" % (
      FLAGS.algorithm,
      FLAGS.timesteps,
      FLAGS.num_agents + FLAGS.num_scripts,
      FLAGS.num_scripts,
      FLAGS.nsteps,
      lr_round,
      start_time)

  if (FLAGS.log == "tensorboard"):
    Logger.DEFAULT \<commit_msg>change learning rate range<commit_after>import sys
import os

from absl import flags
from baselines import deepq
from pysc2.env import sc2_env
from pysc2.lib import actions
import os
from baselines import logger
from baselines.common import set_global_seeds

import deepq_mineral_shards
import datetime

from common.vec_env.subproc_vec_env import SubprocVecEnv
from a2c.policies import CnnPolicy
from a2c import a2c
from baselines.logger import Logger, TensorBoardOutputFormat, HumanOutputFormat

import random

import deepq_mineral_4way

import threading
import time

_MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id
_SELECT_ARMY = actions.FUNCTIONS.select_army.id
_SELECT_ALL = [0]
_NOT_QUEUED = [0]

step_mul = 1

FLAGS = flags.FLAGS
flags.DEFINE_string("map", "CollectMineralShards",
                    "Name of a map to use to play.")
start_time = datetime.datetime.now().strftime("%Y%m%d%H%M")
flags.DEFINE_string("log", "tensorboard", "logging type(stdout, tensorboard)")
flags.DEFINE_string("algorithm", "a2c", "RL algorithm to use.")
flags.DEFINE_integer("timesteps", 2000000, "Steps to train")
flags.DEFINE_float("exploration_fraction", 0.5, "Exploration Fraction")
flags.DEFINE_boolean("prioritized", True, "prioritized_replay")
flags.DEFINE_boolean("dueling", True, "dueling")
flags.DEFINE_float("lr", 0.0005, "Learning rate")
flags.DEFINE_integer("num_agents", 4, "number of RL agents for A2C")
flags.DEFINE_integer("num_scripts", 4, "number of script agents for A2C")
flags.DEFINE_integer("nsteps", 20, "number of batch steps for A2C")

PROJ_DIR = os.path.dirname(os.path.abspath(__file__))

max_mean_reward = 0
last_filename = ""

start_time = datetime.datetime.now().strftime("%m%d%H%M")


def main():
  FLAGS(sys.argv)

  print("algorithm : %s" % FLAGS.algorithm)
  print("timesteps : %s" % FLAGS.timesteps)
  print("exploration_fraction : %s" % FLAGS.exploration_fraction)
  print("prioritized : %s" % FLAGS.prioritized)
  print("dueling : %s" % FLAGS.dueling)
  print("num_agents : %s" % FLAGS.num_agents)
  print("lr : %s" % FLAGS.lr)

  if(FLAGS.lr == 0):
    FLAGS.lr = random.uniform(0.00001, 0.0005)
    
  print("random lr : %s" % FLAGS.lr)

  lr_round = round(FLAGS.lr, 8)

  logdir = "tensorboard"

  if (FLAGS.algorithm == "deepq-4way"):
    logdir = "tensorboard/mineral/%s/%s_%s_prio%s_duel%s_lr%s/%s" % (
      FLAGS.algorithm, FLAGS.timesteps, FLAGS.exploration_fraction,
      FLAGS.prioritized, FLAGS.dueling, lr_round, start_time)
  elif (FLAGS.algorithm == "deepq"):
    logdir = "tensorboard/mineral/%s/%s_%s_prio%s_duel%s_lr%s/%s" % (
      FLAGS.algorithm, FLAGS.timesteps, FLAGS.exploration_fraction,
      FLAGS.prioritized, FLAGS.dueling, lr_round, start_time)
  elif (FLAGS.algorithm == "a2c"):
    logdir = "tensorboard/mineral/%s/%s_n%s_s%s_nsteps%s/lr%s/%s" % (
      FLAGS.algorithm,
      FLAGS.timesteps,
      FLAGS.num_agents + FLAGS.num_scripts,
      FLAGS.num_scripts,
      FLAGS.nsteps,
      lr_round,
      start_time)

  if (FLAGS.log == "tensorboard"):
    Logger.DEFAULT \
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