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,