main.py

import numpy as np
import torch
import gym
import argparse
import os

import utils
import TD3
import OurDDPG
import DDPG


# Runs policy for X episodes and returns average reward
def evaluate_policy(policy, eval_episodes=10):
	avg_reward = 0.
	for _ in xrange(eval_episodes):
		obs = env.reset()
		done = False
		while not done:
			action = policy.select_action(np.array(obs))
			obs, reward, done, _ = env.step(action)
			avg_reward += reward

	avg_reward /= eval_episodes

	print "---------------------------------------"
	print "Evaluation over %d episodes: %f" % (eval_episodes, avg_reward)
	print "---------------------------------------"
	return avg_reward


if __name__ == "__main__":
	
	parser = argparse.ArgumentParser()
	parser.add_argument("--policy_name", default="TD3")					# Policy name
	parser.add_argument("--env_name", default="HalfCheetah-v1")			# OpenAI gym environment name
	parser.add_argument("--seed", default=0, type=int)					# Sets Gym, PyTorch and Numpy seeds
	parser.add_argument("--start_timesteps", default=1e4, type=int)		# How many time steps purely random policy is run for
	parser.add_argument("--eval_freq", default=5e3, type=float)			# How often (time steps) we evaluate
	parser.add_argument("--max_timesteps", default=1e6, type=float)		# Max time steps to run environment for
	parser.add_argument("--save_models", action="store_true")			# Whether or not models are saved
	parser.add_argument("--expl_noise", default=0.1, type=float)		# Std of Gaussian exploration noise
	parser.add_argument("--batch_size", default=100, type=int)			# Batch size for both actor and critic
	parser.add_argument("--discount", default=0.99, type=float)			# Discount factor
	parser.add_argument("--tau", default=0.005, type=float)				# Target network update rate
	parser.add_argument("--policy_noise", default=0.2, type=float)		# Noise added to target policy during critic update
	parser.add_argument("--noise_clip", default=0.5, type=float)		# Range to clip target policy noise
	parser.add_argument("--policy_freq", default=2, type=int)			# Frequency of delayed policy updates
	args = parser.parse_args()

	file_name = "%s_%s_%s" % (args.policy_name, args.env_name, str(args.seed))
	print "---------------------------------------"
	print "Settings: %s" % (file_name)
	print "---------------------------------------"

	if not os.path.exists("./results"):
		os.makedirs("./results")
	if args.save_models and 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
	if args.policy_name == "TD3": policy = TD3.TD3(state_dim, action_dim, max_action)
	elif args.policy_name == "OurDDPG": policy = OurDDPG.DDPG(state_dim, action_dim, max_action)
	elif args.policy_name == "DDPG": policy = DDPG.DDPG(state_dim, action_dim, max_action)

	replay_buffer = utils.ReplayBuffer()
	
	# Evaluate untrained policy
	evaluations = [evaluate_policy(policy)] 

	total_timesteps = 0
	timesteps_since_eval = 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)
				if args.policy_name == "TD3":
					policy.train(replay_buffer, episode_timesteps, args.batch_size, args.discount, args.tau, args.policy_noise, args.noise_clip, args.policy_freq)
				else: 
					policy.train(replay_buffer, episode_timesteps, args.batch_size, args.discount, args.tau)
			
			# Evaluate episode
			if timesteps_since_eval >= args.eval_freq:
				timesteps_since_eval %= args.eval_freq
				evaluations.append(evaluate_policy(policy))
				
				if args.save_models: policy.save(file_name, directory="./pytorch_models")
				np.save("./results/%s" % (file_name), evaluations) 
			
			# 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
		timesteps_since_eval += 1
		
	# Final evaluation 
	evaluations.append(evaluate_policy(policy))
	if args.save_models: policy.save("%s" % (file_name), directory="./pytorch_models")
	np.save("./results/%s" % (file_name), evaluations)