mujoco_env.py

import os
from rllab import spaces
from rllab.envs.base import Env
# from gym import error, spaces
from gym import error
from gym.utils import seeding
import numpy as np
from os import path
import gym
import six

try:
    import mujoco_py
    from mujoco_py.mjlib import mjlib
except ImportError as e:
    raise error.DependencyNotInstalled("{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)".format(e))

class MujocoEnv(Env):
    """Superclass for all MuJoCo environments.
    """

    def __init__(self, model_path, frame_skip):
        if model_path.startswith("/"):
            fullpath = model_path
        else:
            fullpath = os.path.join(os.path.dirname(__file__), model_path)
        if not path.exists(fullpath):
            raise IOError("File %s does not exist" % fullpath)
        self.frame_skip = frame_skip
        self.model = mujoco_py.MjModel(fullpath)
        self.data = self.model.data
        self.viewer = None

        self.metadata = {
            'render.modes': ['human', 'rgb_array'],
            'video.frames_per_second': int(np.round(1.0 / self.dt))
        }

        self.init_qpos = self.model.data.qpos.ravel().copy()
        self.init_qvel = self.model.data.qvel.ravel().copy()
        observation, _reward, done, _info = self.step(np.zeros(self.model.nu))
        assert not done
        self.obs_dim = observation.size

        bounds = self.model.actuator_ctrlrange.copy()
        low = bounds[:, 0]
        high = bounds[:, 1]
        # self.action_space = spaces.Box(low, high)

        high = np.inf*np.ones(self.obs_dim)
        low = -high
        # self.observation_space = spaces.Box(low, high)

        self._seed()

    @property
    def observation_space(self):
        high = np.inf*np.ones(self.obs_dim)
        low = -high
        return spaces.Box(low, high)

    @property
    def action_space(self):
        bounds = self.model.actuator_ctrlrange.copy()
        low = bounds[:, 0]
        high = bounds[:, 1]
        return spaces.Box(low, high)


    def _seed(self, seed=None):
        self.np_random, seed = seeding.np_random(seed)
        return [seed]

    # methods to override:
    # ----------------------------

    def reset_model(self):
        """
        Reset the robot degrees of freedom (qpos and qvel).
        Implement this in each subclass.
        """
        raise NotImplementedError

    def viewer_setup(self):
        """
        This method is called when the viewer is initialized and after every reset
        Optionally implement this method, if you need to tinker with camera position
        and so forth.
        """
        pass

    # -----------------------------
    def reset(self):
        mjlib.mj_resetData(self.model.ptr, self.data.ptr)
        ob = self.reset_model()
        if self.viewer is not None:
            self.viewer.autoscale()
            self.viewer_setup()
        return ob

    # def _reset(self):
    #     mjlib.mj_resetData(self.model.ptr, self.data.ptr)
    #     ob = self.reset_model()
    #     if self.viewer is not None:
    #         self.viewer.autoscale()
    #         self.viewer_setup()
    #     return ob

    def set_state(self, qpos, qvel):
        assert qpos.shape == (self.model.nq,) and qvel.shape == (self.model.nv,)
        self.model.data.qpos = qpos
        self.model.data.qvel = qvel
        self.model._compute_subtree()  # pylint: disable=W0212
        self.model.forward()

    @property
    def dt(self):
        return self.model.opt.timestep * self.frame_skip

    def do_simulation(self, ctrl, n_frames):
        self.model.data.ctrl = ctrl
        for _ in range(n_frames):
            self.model.step()

    # def _render(self, mode='human', close=False):
    #     if close:
    #         if self.viewer is not None:
    #             self._get_viewer().finish()
    #             self.viewer = None
    #         return

    #     if mode == 'rgb_array':
    #         self._get_viewer().render()
    #         data, width, height = self._get_viewer().get_image()
    #         return np.fromstring(data, dtype='uint8').reshape(height, width, 3)[::-1, :, :]
    #     elif mode == 'human':
    #         self._get_viewer().loop_once()
    def render(self, mode='human', close=False):
        if close:
            if self.viewer is not None:
                self._get_viewer().finish()
                self.viewer = None
            return

        if mode == 'rgb_array':
            self._get_viewer().render()
            data, width, height = self._get_viewer().get_image()
            return np.fromstring(data, dtype='uint8').reshape(height, width, 3)[::-1, :, :]
        elif mode == 'human':
            self._get_viewer().loop_once()

    def _get_viewer(self):
        if self.viewer is None:
            self.viewer = mujoco_py.MjViewer()
            self.viewer.start()
            self.viewer.set_model(self.model)
            self.viewer_setup()
        return self.viewer

    def get_body_com(self, body_name):
        idx = self.model.body_names.index(six.b(body_name))
        return self.model.data.com_subtree[idx]

    def get_body_comvel(self, body_name):
        idx = self.model.body_names.index(six.b(body_name))
        return self.model.body_comvels[idx]

    def get_body_xmat(self, body_name):
        idx = self.model.body_names.index(six.b(body_name))
        return self.model.data.xmat[idx].reshape((3, 3))

    def state_vector(self):
        return np.concatenate([
            self.model.data.qpos.flat,
            self.model.data.qvel.flat
        ])