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train_mineral_shards.py
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train_mineral_shards.py
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import sys
import os
import datetime
import random
from absl import flags
from pysc2.env import sc2_env
from pysc2.lib import actions
from baselines_legacy import cnn_to_mlp
from baselines.logger import Logger, TensorBoardOutputFormat, HumanOutputFormat
from common.vec_env.subproc_vec_env import SubprocVecEnv
from a2c.policies import CnnPolicy
from a2c import a2c
import deepq_mineral_4way
import deepq_mineral_shards
_MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id
_SELECT_ARMY = actions.FUNCTIONS.select_army.id
_SELECT_ALL = [0]
_NOT_QUEUED = [0]
step_mul = 8
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", 0, "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 = ""
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 \
= Logger.CURRENT \
= Logger(dir=None,
output_formats=[TensorBoardOutputFormat(logdir)])
elif FLAGS.log == "stdout":
Logger.DEFAULT \
= Logger.CURRENT \
= Logger(dir=None,
output_formats=[HumanOutputFormat(sys.stdout)])
if FLAGS.algorithm == "deepq":
AGENT_INTERFACE_FORMAT = sc2_env.AgentInterfaceFormat(
feature_dimensions=sc2_env.Dimensions(screen=16, minimap=16))
# temp solution - sc2_env.Agent(sc2_env.Race.terran) might be too restricting
# We need this change because sc2 now requires specifying players.
with sc2_env.SC2Env(
map_name="CollectMineralShards",
players=[sc2_env.Agent(sc2_env.Race.terran)],
step_mul=step_mul,
visualize=True,
agent_interface_format=AGENT_INTERFACE_FORMAT) as env:
model = cnn_to_mlp(
convs=[(16, 8, 4), (32, 4, 2)], hiddens=[256], dueling=True)
acts = deepq_mineral_shards.learn(
env,
q_func=model,
num_actions=16,
lr=FLAGS.lr,
max_timesteps=FLAGS.timesteps,
buffer_size=10000,
exploration_fraction=FLAGS.exploration_fraction,
exploration_final_eps=0.01,
train_freq=4,
learning_starts=10000,
target_network_update_freq=1000,
gamma=0.99,
prioritized_replay=True,
callback=deepq_callback)
acts[0].save("mineral_shards_x.pkl")
acts[1].save("mineral_shards_y.pkl")
elif FLAGS.algorithm == "deepq-4way":
AGENT_INTERFACE_FORMAT = sc2_env.AgentInterfaceFormat(
feature_dimensions=sc2_env.Dimensions(screen=32, minimap=32))
with sc2_env.SC2Env(
map_name="CollectMineralShards",
step_mul=step_mul,
agent_interface_format=AGENT_INTERFACE_FORMAT,
visualize=True) as env:
model = cnn_to_mlp(
convs=[(16, 8, 4), (32, 4, 2)], hiddens=[256], dueling=True)
act = deepq_mineral_4way.learn(
env,
q_func=model,
num_actions=4,
lr=FLAGS.lr,
max_timesteps=FLAGS.timesteps,
buffer_size=10000,
exploration_fraction=FLAGS.exploration_fraction,
exploration_final_eps=0.01,
train_freq=4,
learning_starts=10000,
target_network_update_freq=1000,
gamma=0.99,
prioritized_replay=True,
callback=deepq_4way_callback)
act.save("mineral_shards.pkl")
elif FLAGS.algorithm == "a2c":
num_timesteps = int(40e6)
num_timesteps //= 4
seed = 0
env = SubprocVecEnv(FLAGS.num_agents + FLAGS.num_scripts, FLAGS.num_scripts,
FLAGS.map)
policy_fn = CnnPolicy
a2c.learn(
policy_fn,
env,
seed,
total_timesteps=num_timesteps,
nprocs=FLAGS.num_agents + FLAGS.num_scripts,
nscripts=FLAGS.num_scripts,
ent_coef=0.5,
nsteps=FLAGS.nsteps,
max_grad_norm=0.01,
callback=a2c_callback)
def deepq_callback(locals, globals):
global max_mean_reward, last_filename
if 'done' in locals and locals['done'] == True:
if 'mean_100ep_reward' in locals and locals['num_episodes'] >= 10\
and locals['mean_100ep_reward'] > max_mean_reward:
print("mean_100ep_reward : %s max_mean_reward : %s" %
(locals['mean_100ep_reward'], max_mean_reward))
if not os.path.exists(os.path.join(PROJ_DIR, 'models/deepq/')):
try:
os.mkdir(os.path.join(PROJ_DIR, 'models/'))
except Exception as e:
print(str(e))
try:
os.mkdir(os.path.join(PROJ_DIR, 'models/deepq/'))
except Exception as e:
print(str(e))
if last_filename != "":
os.remove(last_filename)
print("delete last model file : %s" % last_filename)
max_mean_reward = locals['mean_100ep_reward']
act_x = deepq_mineral_shards.ActWrapper(locals['act_x'])
act_y = deepq_mineral_shards.ActWrapper(locals['act_y'])
filename = os.path.join(
PROJ_DIR,
'models/deepq/mineral_x_%s.pkl' % locals['mean_100ep_reward'])
act_x.save(filename)
filename = os.path.join(
PROJ_DIR,
'models/deepq/mineral_y_%s.pkl' % locals['mean_100ep_reward'])
act_y.save(filename)
print("save best mean_100ep_reward model to %s" % filename)
last_filename = filename
def deepq_4way_callback(locals, globals):
global max_mean_reward, last_filename
if 'done' in locals and locals['done'] == True:
if 'mean_100ep_reward' in locals and locals['num_episodes'] >= 10\
and locals['mean_100ep_reward'] > max_mean_reward:
print("mean_100ep_reward : %s max_mean_reward : %s" %
(locals['mean_100ep_reward'], max_mean_reward))
if not os.path.exists(
os.path.join(PROJ_DIR, 'models/deepq-4way/')):
try:
os.mkdir(os.path.join(PROJ_DIR, 'models/'))
except Exception as e:
print(str(e))
try:
os.mkdir(os.path.join(PROJ_DIR, 'models/deepq-4way/'))
except Exception as e:
print(str(e))
if last_filename != "":
os.remove(last_filename)
print("delete last model file : %s" % last_filename)
max_mean_reward = locals['mean_100ep_reward']
act = deepq_mineral_4way.ActWrapper(locals['act'])
# act_y = deepq_mineral_shards.ActWrapper(locals['act_y'])
filename = os.path.join(PROJ_DIR,
'models/deepq-4way/mineral_%s.pkl' %
locals['mean_100ep_reward'])
act.save(filename)
print("save best mean_100ep_reward model to %s" % filename)
last_filename = filename
def a2c_callback(locals, globals):
global max_mean_reward, last_filename
if 'mean_100ep_reward' in locals and locals['num_episodes'] >= 10\
and locals['mean_100ep_reward'] > max_mean_reward:
print("mean_100ep_reward : %s max_mean_reward : %s" %
(locals['mean_100ep_reward'], max_mean_reward))
if not os.path.exists(os.path.join(PROJ_DIR, 'models/a2c/')):
try:
os.mkdir(os.path.join(PROJ_DIR, 'models/'))
except Exception as e:
print(str(e))
try:
os.mkdir(os.path.join(PROJ_DIR, 'models/a2c/'))
except Exception as e:
print(str(e))
if last_filename != "":
os.remove(last_filename)
print("delete last model file : %s" % last_filename)
max_mean_reward = locals['mean_100ep_reward']
model = locals['model']
filename = os.path.join(
PROJ_DIR,
'models/a2c/mineral_%s.pkl' % locals['mean_100ep_reward'])
model.save(filename)
print("save best mean_100ep_reward model to %s" % filename)
last_filename = filename
if __name__ == '__main__':
main()