Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use tdobrxl/deeprl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use tdobrxl/deeprl with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="tdobrxl/deeprl", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
|
Download README.md from tdobrxl/deeprl: direct link, hf CLI and curl.
- Browser
- Download file 784 Bytes
-
https://huggingface.co/tdobrxl/deeprl/resolve/main/README.md
- Command line
-
hf download hf://tdobrxl/deeprl/README.md
-
curl -L -o README.md https://huggingface.co/tdobrxl/deeprl/resolve/main/README.md
784 Bytes
metadata
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: MLP
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 216.33 +/- 82.67
name: mean_reward
verified: false
MLP Agent playing LunarLander-v2
This is a trained model of a MLP agent playing LunarLander-v2 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...