""" 
Implementation of Meta Policy Gradient

Algorithm and hyperparameter details can be found here: 

    http://arxiv.org/pdf/1509.02971v2.pdf

The algorithm is tested on the Pendulum-v0 OpenAI gym task 
and developed with tflearn + Tensorflow

Author: <NAME> 
"""
import tensorflow as tf
import numpy as np
import gym
from gym import wrappers
import tflearn
import argparse
import pprint as pp

# ===========================
#   Tensorflow Summary Ops
# ===========================

def build_summaries():
    episode_reward = tf.Variable(0.)
    tf.summary.scalar("Reward", episode_reward)
    episode_ave_max_q = tf.Variable(0.)
    tf.summary.scalar("Qmax Value", episode_ave_max_q)

    summary_vars = [episode_reward, episode_ave_max_q]
    summary_ops = tf.summary.merge_all()

    return summary_ops, summary_vars

# ===========================
#   Agent Training
# ===========================

def train(sess, env, args, actor, critic, actor_noise):

    # Set up summary Ops
    summary_ops, summary_vars = build_summaries()

    sess.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter(args['summary_dir'], sess.graph)

    # TODO add the rest of training

def main(args):

    with tf.Session() as sess:

        env = gym.make(args['env'])
        np.random.seed(int(args['random_seed']))
        tf.set_random_seed(int(args['random_seed']))
        env.seed(int(args['random_seed']))

        state_dim = env.observation_space.shape[0]
        action_dim = env.action_space.shape[0]
        action_bound = env.action_space.high
        # Ensure action bound is symmetric
        assert (env.action_space.high == -env.action_space.low)

        train(sess, env, args, actor, critic, actor_noise)

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='provide arguments for DDPG agent')

    # agent parameters
    parser.add_argument('--actor-lr', help='actor network learning rate', default=0.0001)
    parser.add_argument('--critic-lr', help='critic network learning rate', default=0.001)
    parser.add_argument('--gamma', help='discount factor for critic updates', default=0.99)
    parser.add_argument('--tau', help='soft target update parameter', default=0.001)
    parser.add_argument('--buffer-size', help='max size of the replay buffer', default=1000000)
    parser.add_argument('--minibatch-size', help='size of minibatch for minibatch-SGD', default=64)

    # run parameters
    parser.add_argument('--env', help='choose the gym env- tested on {Pendulum-v0}', default='Pendulum-v0')
    parser.add_argument('--random-seed', help='random seed for repeatability', default=1234)
    parser.add_argument('--max-episodes', help='max num of episodes to do while training', default=50000)
    parser.add_argument('--max-episode-len', help='max length of 1 episode', default=1000)
    parser.add_argument('--render-env', help='render the gym env', action='store_true')
    parser.add_argument('--summary-dir', help='directory for storing tensorboard info', default='./results/tf_meta')

    parser.set_defaults(render_env=False)
    parser.set_defaults(use_gym_monitor=True)
    
    args = vars(parser.parse_args())
    
    pp.pprint(args)

    main(args)

""" 
Implementation of Meta Policy Gradient

Algorithm and hyperparameter details can be found here: 

    http://arxiv.org/pdf/1509.02971v2.pdf

The algorithm is tested on the Pendulum-v0 OpenAI gym task 
and developed with tflearn + Tensorflow

Author: <NAME> 
"""
import tensorflow as tf
import numpy as np
import gym
from gym import wrappers
import tflearn
import argparse
import pprint as pp

# ===========================
#   Tensorflow Summary Ops
# ===========================

def build_summaries():
    episode_reward = tf.Variable(0.)
    tf.summary.scalar("Reward", episode_reward)
    episode_ave_max_q = tf.Variable(0.)
    tf.summary.scalar("Qmax Value", episode_ave_max_q)

    summary_vars = [episode_reward, episode_ave_max_q]
    summary_ops = tf.summary.merge_all()

    return summary_ops, summary_vars

# ===========================
#   Agent Training
# ===========================

def train(sess, env, args, actor, critic, actor_noise):

    # Set up summary Ops
    summary_ops, summary_vars = build_summaries()

    sess.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter(args['summary_dir'], sess.graph)

    # TODO add the rest of training

def main(args):

    with tf.Session() as sess:

        env = gym.make(args['env'])
        np.random.seed(int(args['random_seed']))
        tf.set_random_seed(int(args['random_seed']))
        env.seed(int(args['random_seed']))

        state_dim = env.observation_space.shape[0]
        action_dim = env.action_space.shape[0]
        action_bound = env.action_space.high
        # Ensure action bound is symmetric
        assert (env.action_space.high == -env.action_space.low)

        train(sess, env, args, actor, critic, actor_noise)

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='provide arguments for DDPG agent')

    # agent parameters
    parser.add_argument('--actor-lr', help='actor network learning rate', default=0.0001)
    parser.add_argument('--critic-lr', help='critic network learning rate', default=0.001)
    parser.add_argument('--gamma', help='discount factor for critic updates', default=0.99)
    parser.add_argument('--tau', help='soft target update parameter', default=0.001)
    parser.add_argument('--buffer-size', help='max size of the replay buffer', default=1000000)
    parser.add_argument('--minibatch-size', help='size of minibatch for minibatch-SGD', default=64)

    # run parameters
    parser.add_argument('--env', help='choose the gym env- tested on {Pendulum-v0}', default='Pendulum-v0')
    parser.add_argument('--random-seed', help='random seed for repeatability', default=1234)
    parser.add_argument('--max-episodes', help='max num of episodes to do while training', default=50000)
    parser.add_argument('--max-episode-len', help='max length of 1 episode', default=1000)
    parser.add_argument('--render-env', help='render the gym env', action='store_true')
    parser.add_argument('--summary-dir', help='directory for storing tensorboard info', default='./results/tf_meta')

    parser.set_defaults(render_env=False)
    parser.set_defaults(use_gym_monitor=True)
    
    args = vars(parser.parse_args())
    
    pp.pprint(args)

    main(args)

Implementation of Meta Policy Gradient  Algorithm and hyperparameter details can be found here:       http://arxiv.org/pdf/1509.02971v2.pdf  The algorithm is tested on the Pendulum-v0 OpenAI gym task  and developed with tflearn + Tensorflow  Author: <NAME> # =========================== #   Tensorflow Summary Ops # =========================== # =========================== #   Agent Training # =========================== # Set up summary Ops # TODO add the rest of training # Ensure action bound is symmetric # agent parameters # run parameters