Reinforcement Learning
stable-baselines3
BipedalWalker-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use bvk1ng/bipedal_walker_ppo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use bvk1ng/bipedal_walker_ppo with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="bvk1ng/bipedal_walker_ppo", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
metadata
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: Proximal Policy Optimisation (PPO)
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
metrics:
- type: mean_reward
value: 209.63 +/- 82.30
name: mean_reward
verified: false
Proximal Policy Optimisation (PPO) Agent playing BipedalWalker-v3
This is a trained model of a Proximal Policy Optimisation (PPO) agent playing BipedalWalker-v3 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...