#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import gym
import numpy as np
import time
import parl
from mujoco_agent import MujocoAgent
from mujoco_model import MujocoModel
from parl.utils import logger, action_mapping, ReplayMemory

ACTOR_LR = 1e-4
CRITIC_LR = 1e-3
GAMMA = 0.99
TAU = 0.001
MEMORY_SIZE = int(1e6)
MEMORY_WARMUP_SIZE = 1e4
BATCH_SIZE = 128
REWARD_SCALE = 0.1
ENV_SEED = 1


def run_train_episode(env, agent, rpm):
    obs = env.reset()
    total_reward = 0
    while True:
        batch_obs = np.expand_dims(obs, axis=0)
        action = agent.predict(batch_obs.astype('float32'))

        # Add exploration noise, and clip to [-1.0, 1.0]
        action = np.clip(np.random.normal(action, 1.0), -1.0, 1.0)
        action = action_mapping(action, env.action_space.low[0],
                                env.action_space.high[0])

        next_obs, reward, done, info = env.step(action)

        rpm.append(obs, action, REWARD_SCALE * reward, next_obs, done)

        if rpm.size() > MEMORY_WARMUP_SIZE:
            batch_obs, batch_action, batch_reward, batch_next_obs, batch_terminal = rpm.sample_batch(
                BATCH_SIZE)
            agent.learn(batch_obs, batch_action, batch_reward, batch_next_obs,
                        batch_terminal)

        obs = next_obs
        total_reward += reward

        if done:
            break
    return total_reward


def run_evaluate_episode(env, agent):
    obs = env.reset()
    total_reward = 0
    while True:
        batch_obs = np.expand_dims(obs, axis=0)
        action = agent.predict(batch_obs.astype('float32'))
        action = action_mapping(action, env.action_space.low[0],
                                env.action_space.high[0])

        next_obs, reward, done, info = env.step(action)

        obs = next_obs
        total_reward += reward

        if done:
            break
    return total_reward


def main():
    env = gym.make(args.env)
    env.seed(ENV_SEED)

    obs_dim = env.observation_space.shape[0]
    act_dim = env.action_space.shape[0]

    model = MujocoModel(act_dim)
    algorithm = parl.algorithms.DDPG(
        model, gamma=GAMMA, tau=TAU, actor_lr=ACTOR_LR, critic_lr=CRITIC_LR)
    agent = MujocoAgent(algorithm, obs_dim, act_dim)

    rpm = ReplayMemory(MEMORY_SIZE, obs_dim, act_dim)

    while rpm.size() < MEMORY_WARMUP_SIZE:
        run_train_episode(env, agent, rpm)

    episode = 0
    while episode < args.train_total_episode:
        for i in range(50):
            train_reward = run_train_episode(env, agent, rpm)
            episode += 1
            logger.info('Episode: {} Reward: {}'.format(episode, train_reward))

        evaluate_reward = run_evaluate_episode(env, agent)
        logger.info('Episode {}, Evaluate reward: {}'.format(
            episode, evaluate_reward))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--env', help='Mujoco environment name', default='HalfCheetah-v2')
    parser.add_argument(
        '--train_total_episode',
        type=int,
        default=int(1e4),
        help='maximum training episodes')

    args = parser.parse_args()

    main()