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https://github.com/Farama-Foundation/Gymnasium.git
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a9d7fc7dd1
* remove vertical camera motion on env.reset() * In MuJoCo envs, don't reset viewers on environment reset. If the user moves around the camera in the viewer, allow them to keep the camera wherever they put it. * Docstring update.
146 lines
4.7 KiB
Python
146 lines
4.7 KiB
Python
import os
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from gym import error, spaces
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from gym.utils import seeding
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import numpy as np
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from os import path
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import gym
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import six
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try:
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import mujoco_py
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except ImportError as e:
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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))
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DEFAULT_SIZE = 500
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class MujocoEnv(gym.Env):
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"""Superclass for all MuJoCo environments.
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"""
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def __init__(self, model_path, frame_skip):
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if model_path.startswith("/"):
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fullpath = model_path
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else:
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fullpath = os.path.join(os.path.dirname(__file__), "assets", model_path)
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if not path.exists(fullpath):
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raise IOError("File %s does not exist" % fullpath)
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self.frame_skip = frame_skip
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self.model = mujoco_py.load_model_from_path(fullpath)
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self.sim = mujoco_py.MjSim(self.model)
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self.data = self.sim.data
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self.viewer = None
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self._viewers = {}
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self.metadata = {
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'render.modes': ['human', 'rgb_array', 'depth_array'],
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'video.frames_per_second': int(np.round(1.0 / self.dt))
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}
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self.init_qpos = self.sim.data.qpos.ravel().copy()
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self.init_qvel = self.sim.data.qvel.ravel().copy()
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observation, _reward, done, _info = self.step(np.zeros(self.model.nu))
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assert not done
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self.obs_dim = observation.size
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bounds = self.model.actuator_ctrlrange.copy()
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low = bounds[:, 0]
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high = bounds[:, 1]
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self.action_space = spaces.Box(low=low, high=high, dtype=np.float32)
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high = np.inf*np.ones(self.obs_dim)
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low = -high
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self.observation_space = spaces.Box(low, high, dtype=np.float32)
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self.seed()
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def seed(self, seed=None):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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# methods to override:
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# ----------------------------
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def reset_model(self):
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"""
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Reset the robot degrees of freedom (qpos and qvel).
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Implement this in each subclass.
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"""
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raise NotImplementedError
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def viewer_setup(self):
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"""
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This method is called when the viewer is initialized.
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Optionally implement this method, if you need to tinker with camera position
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and so forth.
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"""
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pass
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# -----------------------------
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def reset(self):
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self.sim.reset()
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ob = self.reset_model()
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return ob
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def set_state(self, qpos, qvel):
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assert qpos.shape == (self.model.nq,) and qvel.shape == (self.model.nv,)
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old_state = self.sim.get_state()
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new_state = mujoco_py.MjSimState(old_state.time, qpos, qvel,
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old_state.act, old_state.udd_state)
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self.sim.set_state(new_state)
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self.sim.forward()
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@property
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def dt(self):
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return self.model.opt.timestep * self.frame_skip
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def do_simulation(self, ctrl, n_frames):
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self.sim.data.ctrl[:] = ctrl
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for _ in range(n_frames):
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self.sim.step()
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def render(self, mode='human', width=DEFAULT_SIZE, height=DEFAULT_SIZE):
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if mode == 'rgb_array':
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self._get_viewer(mode).render(width, height)
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# window size used for old mujoco-py:
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data = self._get_viewer(mode).read_pixels(width, height, depth=False)
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# original image is upside-down, so flip it
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return data[::-1, :, :]
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elif mode == 'depth_array':
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self._get_viewer(mode).render(width, height)
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# window size used for old mujoco-py:
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# Extract depth part of the read_pixels() tuple
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data = self._get_viewer(mode).read_pixels(width, height, depth=True)[1]
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# original image is upside-down, so flip it
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return data[::-1, :]
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elif mode == 'human':
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self._get_viewer(mode).render()
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def close(self):
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if self.viewer is not None:
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# self.viewer.finish()
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self.viewer = None
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self._viewers = {}
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def _get_viewer(self, mode):
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self.viewer = self._viewers.get(mode)
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if self.viewer is None:
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if mode == 'human':
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self.viewer = mujoco_py.MjViewer(self.sim)
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elif mode == 'rgb_array' or mode == 'depth_array':
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self.viewer = mujoco_py.MjRenderContextOffscreen(self.sim, -1)
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self.viewer_setup()
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self._viewers[mode] = self.viewer
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return self.viewer
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def get_body_com(self, body_name):
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return self.data.get_body_xpos(body_name)
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def state_vector(self):
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return np.concatenate([
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self.sim.data.qpos.flat,
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self.sim.data.qvel.flat
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])
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