<reponame>Thibaud-Ardoin/d4rl_evaluations
import numpy as np
import torch
import gym
import argparse
import os

import utils
import DDPG

# Shortened version of code originally found at https://github.com/sfujim/TD3

if __name__ == "__main__":

	parser = argparse.ArgumentParser()
	parser.add_argument("--env_name", default="Hopper-v1")				# OpenAI gym environment name
	parser.add_argument("--seed", default=0, type=int)					# Sets Gym, PyTorch and Numpy seeds
	parser.add_argument("--max_timesteps", default=1e6, type=float)		# Max time steps to run environment for
	parser.add_argument("--start_timesteps", default=1e3, type=int)		# How many time steps purely random policy is run for
	parser.add_argument("--expl_noise", default=0.1, type=float)		# Std of Gaussian exploration noise
	args = parser.parse_args()

	file_name = "DDPG_%s_%s" % (args.env_name, str(args.seed))
	buffer_name = "Final_%s_%s" % (args.env_name, str(args.seed))
	print "---------------------------------------"
	print "Settings: " + file_name
	print "---------------------------------------"

	if not os.path.exists("./pytorch_models"):
		os.makedirs("./pytorch_models")

	env = gym.make(args.env_name)

	# Set seeds
	env.seed(args.seed)
	torch.manual_seed(args.seed)
	np.random.seed(args.seed)

	state_dim = env.observation_space.shape[0]
	action_dim = env.action_space.shape[0]
	max_action = float(env.action_space.high[0])

	# Initialize policy and buffer
	policy = DDPG.DDPG(state_dim, action_dim, max_action)
	replay_buffer = utils.ReplayBuffer()

	total_timesteps = 0
	episode_num = 0
	done = True

	while total_timesteps < args.max_timesteps:

		if done:

			if total_timesteps != 0:
				print("Total T: %d Episode Num: %d Episode T: %d Reward: %f") % (total_timesteps, episode_num, episode_timesteps, episode_reward)
				policy.train(replay_buffer, episode_timesteps)

			# Save policy
			if total_timesteps % 1e5 == 0:
				policy.save(file_name, directory="./pytorch_models")

			# Reset environment
			obs = env.reset()
			done = False
			episode_reward = 0
			episode_timesteps = 0
			episode_num += 1

		# Select action randomly or according to policy
		if total_timesteps < args.start_timesteps:
			action = env.action_space.sample()
		else:
			action = policy.select_action(np.array(obs))
			if args.expl_noise != 0:
				action = (action + np.random.normal(0, args.expl_noise, size=env.action_space.shape[0])).clip(env.action_space.low, env.action_space.high)

		# Perform action
		new_obs, reward, done, _ = env.step(action)
		done_bool = 0 if episode_timesteps + 1 == env._max_episode_steps else float(done)
		episode_reward += reward

		# Store data in replay buffer
		replay_buffer.add((obs, new_obs, action, reward, done_bool))

		obs = new_obs

		episode_timesteps += 1
		total_timesteps += 1

	# Save final policy
	policy.save("%s" % (file_name), directory="./pytorch_models")
	replay_buffer.save(buffer_name)
