Source code for rofunc.learning.RofuncRL.tasks.isaacgymenv.ase.humanoid_amp_task
# Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import torch
from rofunc.learning.RofuncRL.tasks.isaacgymenv.ase.humanoid_amp import HumanoidAMP
[docs]class HumanoidAMPTask(HumanoidAMP):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self._enable_task_obs = cfg["env"]["enableTaskObs"]
super().__init__(cfg=self.cfg, rl_device=rl_device, sim_device=sim_device,
graphics_device_id=graphics_device_id, headless=headless,
virtual_screen_capture=virtual_screen_capture, force_render=force_render)
[docs] def get_obs_size(self):
obs_size = super().get_obs_size()
if self._enable_task_obs:
task_obs_size = self.get_task_obs_size()
obs_size += task_obs_size
return obs_size
[docs] def get_task_obs_size(self):
return 0
[docs] def pre_physics_step(self, actions):
super().pre_physics_step(actions)
self._update_task()
[docs] def render(self, sync_frame_time=False):
super().render(sync_frame_time)
if self.viewer:
self._draw_task()
def _update_task(self):
return
[docs] def reset_idx(self, env_ids):
super().reset_idx(env_ids)
self._reset_task(env_ids)
def _reset_task(self, env_ids):
pass
def _compute_observations(self, env_ids=None):
humanoid_obs = self._compute_humanoid_obs(env_ids)
if self._enable_task_obs:
task_obs = self._compute_task_obs(env_ids)
obs = torch.cat([humanoid_obs, task_obs], dim=-1)
else:
obs = humanoid_obs
if env_ids is None:
self.obs_buf[:] = obs
else:
self.obs_buf[env_ids] = obs
def _compute_task_obs(self, env_ids=None):
return NotImplemented
def _compute_reward(self, actions):
return NotImplemented
def _draw_task(self):
pass