fancy_gym/alr_envs/alr/mujoco/hopper_jump/mp_wrapper.py
2022-04-20 14:50:02 +02:00

58 lines
1.6 KiB
Python

from typing import Tuple, Union
import numpy as np
from mp_env_api import MPEnvWrapper
class MPWrapper(MPEnvWrapper):
@property
def active_obs(self):
return np.hstack([
[False] * (5 + int(not self.exclude_current_positions_from_observation)), # position
[False] * 6, # velocity
[True]
])
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
return self.env.sim.data.qpos[3:6].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.sim.data.qvel[3:6].copy()
@property
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
@property
def dt(self) -> Union[float, int]:
return self.env.dt
class HighCtxtMPWrapper(MPWrapper):
@property
def active_obs(self):
return np.hstack([
[True] * (5 + int(not self.exclude_current_positions_from_observation)), # position
[False] * 6, # velocity
[False]
])
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
return self.env.sim.data.qpos[3:6].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.sim.data.qvel[3:6].copy()
@property
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
@property
def dt(self) -> Union[float, int]:
return self.env.dt