Narsil-Dinghuai Zhang 张鼎怀 zdhNarsil

This short article talks about the interpretation of each dimension of the observation space of PyBullet envs obtained using import pybullet_envs.

Let's use HalfCheetahBulletEnv-v0 as an example.

At the time of writing, pybullet_envs can be found here:

If we look into its __init__.py, we see that HalfCheetahBulletEnv-v0 is registered as:

	import torch

	torch.set_float32_matmul_precision("high")

	from diffusers import StableDiffusion3Pipeline
	import time

	id = "stabilityai/stable-diffusion-3-medium-diffusers"
	pipeline = StableDiffusion3Pipeline.from_pretrained(
	id,

	#!/usr/bin/env python

	import math
	import matplotlib.pyplot as plt
	import torch
	import torch.nn as nn

	from sklearn.datasets import make_moons
	from torch import Tensor
	from tqdm import tqdm

	import numpy as np
	import bisect
	from sklearn.preprocessing import LabelEncoder, MultiLabelBinarizer
	from sklearn.utils.validation import check_is_fitted


	class InductiveConformalPredictor():
	"""
	Standard Conformal Predictor with uncertainty non-conformity score.

	from sklearn.model_selection import train_test_split
	from sklearn.linear_model import LogisticRegression
	from conformal.conformal_predictor import InductiveConformalPredictor

	data = load_digits()
	X, y = data.data, data.target
	alpha = 0.05
	seed = 41

	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,

	import logging
	from foolbox.utils import onehot_like

	from foolbox.attacks.base import Attack
	from foolbox.attacks.base import generator_call_decorator

	import numpy as np
	from numba import jitclass

	import line_profiler

	import imageio
	import numpy as np
	import os

	from collections import defaultdict
	from torch.utils.data import Dataset

	from tqdm.autonotebook import tqdm

	dir_structure_help = r"""