Instructions to use Subhash3008/m-agents-SoccerTwos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use Subhash3008/m-agents-SoccerTwos with ml-agents:
mlagents-load-from-hf --repo-id="Subhash3008/m-agents-SoccerTwos" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
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Download README.md from Subhash3008/m-agents-SoccerTwos: direct link, hf CLI and curl.
- Browser
- Download file 679 Bytes
-
https://huggingface.co/Subhash3008/m-agents-SoccerTwos/resolve/main/README.md
- Command line
-
hf download hf://Subhash3008/m-agents-SoccerTwos/README.md
-
curl -L -o README.md https://huggingface.co/Subhash3008/m-agents-SoccerTwos/resolve/main/README.md
679 Bytes
metadata
tags:
- reinforcement-learning
- ML-Agents-SoccerTwos
- ml-agents
- deep-rl-course
model-index:
- name: m-agents-SoccerTwos
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ML-Agents-SoccerTwos
type: ML-Agents-SoccerTwos
metrics:
- type: mean_reward
value: 1.8 +/- 0.2
name: mean_reward
ML-Agents SoccerTwos
This is a trained model for the Hugging Face Deep Reinforcement Learning Course.
- Environment:
ML-Agents-SoccerTwos - Library:
ml-agents - Evaluation Score:
1.8 +/- 0.2
Trained and submitted by Subhash3008.