import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
import os


class CoupledHalfCheetah(mujoco_env.MujocoEnv, utils.EzPickle):

    def __init__(self, **kwargs):
        mujoco_env.MujocoEnv.__init__(
            self, os.path.join(os.path.dirname(os.path.abspath(__file__)), 'assets', 'coupled_half_cheetah.xml'), 5
        )
        utils.EzPickle.__init__(self)

    def step(self, action):
        xposbefore1 = self.sim.data.qpos[0]
        xposbefore2 = self.sim.data.qpos[len(self.sim.data.qpos) // 2]
        self.do_simulation(action, self.frame_skip)
        xposafter1 = self.sim.data.qpos[0]
        xposafter2 = self.sim.data.qpos[len(self.sim.data.qpos) // 2]
        ob = self._get_obs()
        reward_ctrl1 = -0.1 * np.square(action[0:len(action) // 2]).sum()
        reward_ctrl2 = -0.1 * np.square(action[len(action) // 2:]).sum()
        reward_run1 = (xposafter1 - xposbefore1) / self.dt
        reward_run2 = (xposafter2 - xposbefore2) / self.dt
        reward = (reward_ctrl1 + reward_ctrl2) / 2.0 + (reward_run1 + reward_run2) / 2.0
        done = False
        return ob, reward, done, dict(
            reward_run1=reward_run1, reward_ctrl1=reward_ctrl1, reward_run2=reward_run2, reward_ctrl2=reward_ctrl2
        )

    def _get_obs(self):
        return np.concatenate([
            self.sim.data.qpos.flat[1:],
            self.sim.data.qvel.flat,
        ])

    def reset_model(self):
        qpos = self.init_qpos + self.np_random.uniform(low=-.1, high=.1, size=self.model.nq)
        qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1
        self.set_state(qpos, qvel)
        return self._get_obs()

    def viewer_setup(self):
        self.viewer.cam.distance = self.model.stat.extent * 0.5

    def get_env_info(self):
        return {"episode_limit": self.episode_limit}