Instructions to use mkahari/RL_testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkahari/RL_testing with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mkahari/RL_testing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from mkahari/RL_testing: direct link, hf CLI and curl.
- Browser
- Download file 722 Bytes
-
https://huggingface.co/mkahari/RL_testing/resolve/main/README.md
- Command line
-
hf download hf://mkahari/RL_testing/README.md
-
curl -L -o README.md https://huggingface.co/mkahari/RL_testing/resolve/main/README.md
722 Bytes
metadata
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: RL_testing
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.70
name: mean_reward
verified: false
PPO Agent playing LunarLander-v2
This is a trained model of a PPO 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
...