mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
from gym.spaces import Box
|
|
import numpy as np
|
|
|
|
from gym.envs.classic_control import CartPoleEnv
|
|
|
|
|
|
class StatelessCartPole(CartPoleEnv):
|
|
"""Partially observable variant of the CartPole gym environment.
|
|
|
|
https://github.com/openai/gym/blob/master/gym/envs/classic_control/
|
|
cartpole.py
|
|
|
|
We delete the x- and angular velocity components of the state, so that it
|
|
can only be solved by a memory enhanced model (policy).
|
|
"""
|
|
|
|
def __init__(self, config=None):
|
|
super().__init__()
|
|
|
|
# Fix our observation-space (remove 2 velocity components).
|
|
high = np.array(
|
|
[
|
|
self.x_threshold * 2,
|
|
self.theta_threshold_radians * 2,
|
|
],
|
|
dtype=np.float32)
|
|
|
|
self.observation_space = Box(low=-high, high=high, dtype=np.float32)
|
|
|
|
def step(self, action):
|
|
next_obs, reward, done, info = super().step(action)
|
|
# next_obs is [x-pos, x-veloc, angle, angle-veloc]
|
|
return np.array([next_obs[0], next_obs[2]]), reward, done, info
|
|
|
|
def reset(self):
|
|
init_obs = super().reset()
|
|
# init_obs is [x-pos, x-veloc, angle, angle-veloc]
|
|
return np.array([init_obs[0], init_obs[2]])
|