def main(gin_file, gin_params, log_dir, prev_log, google_colab):
eager_setup()
gin.parse_config_file(gin_file)
if gin_params:
gin_params_flat = [param[0] for param in gin_params]
gin.parse_config_files_and_bindings([params.gin_file], gin_params_flat)
train_eval(log_dir=log_dir, prev_log=prev_log, google_colab=google_colab)
import numpy as np
import gym
# from gym.wrappers import Monitor
import argparse
import tensorflow as tf
import matplotlib.pylab as plt
from tf_rl.common.utils import eager_setup
from tf_rl.agents.DDPG import DDPG
from tf_rl.common.networks import DDPG_Actor as Actor, DDPG_Critic as Critic
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
eager_setup()
parser = argparse.ArgumentParser()
parser.add_argument("--env_name", default="Ant-v2", help="Env title")
parser.add_argument("--seed",
default=123,
type=int,
help="seed for randomness")
parser.add_argument("--num_frames",
default=1_000_000,
type=int,
help="total frame in a training")
parser.add_argument("--train_interval",
default=100,
type=int,
help="a frequency of training in training phase")
parser.add_argument("--nb_train_steps",
default=50,
