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
Download LICENSE from hwihwalab/cartpole-v1-ppo: direct link, hf CLI and curl.
- Browser
- Download file 1.08 kB
-
https://huggingface.co/hwihwalab/cartpole-v1-ppo/resolve/main/LICENSE
- Command line
-
hf download hf://hwihwalab/cartpole-v1-ppo/LICENSE
-
curl -L -o LICENSE https://huggingface.co/hwihwalab/cartpole-v1-ppo/resolve/main/LICENSE
1.08 kB
| MIT License | |
| Copyright (c) 2026 Hwihwa-Lab (hwihwalab) | |
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