Reinforcement Learning
stable-baselines3
PyTorch
English
Korean
deep-reinforcement-learning
ppo
proximal-policy-optimization
cartpole-v1
gymnasium
openai-gym
classic-control
robotics
physical-ai
sim-to-real
zero-shot-transfer
domain-randomization
inverted-pendulum
optimal-control
lqr-control
control-theory
interactive-demo
Eval Results (legacy)
Instructions to use hwihwalab/cartpole-v1-ppo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use hwihwalab/cartpole-v1-ppo with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="hwihwalab/cartpole-v1-ppo", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
File size: 1,079 Bytes
a41bf60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | MIT License
Copyright (c) 2026 Hwihwa-Lab (hwihwalab)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|