from ddpg_agent import ddpg_agent
from arguments import get_args
from rl_utils.seeds.seeds import set_seeds
from rl_utils.env_wrapper.create_env import create_single_env
from mpi4py import MPI
import os
if __name__ == '__main__':
# set thread and mpi stuff
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['IN_MPI'] = '1'
# train the network
args = get_args()
# build up the environment
env = create_single_env(args, MPI.COMM_WORLD.Get_rank())
# set the random seeds
set_seeds(args, MPI.COMM_WORLD.Get_rank())
# start traininng
ddpg_trainer = ddpg_agent(env, args)
ddpg_trainer.learn()
# close the environment
env.close()
import sys
from arguments import get_args
from rl_utils.env_wrapper.create_env import create_single_env
from rl_utils.logger import logger, bench
from rl_utils.seeds.seeds import set_seeds
from dqn_agent import dqn_agent
import os
import numpy as np
if __name__ == '__main__':
# get arguments
args = get_args()
# start to create the environment
env = create_single_env(args)
# set seeds
set_seeds(args)
# create trainer
dqn_trainer = dqn_agent(env, args)
# start to learn
dqn_trainer.learn()
# finally - close the environment
env.close()