Reinforcement Learning
ml-agents
TensorBoard
ONNX
ML-Agents-Pyramids
unity-ml-agents
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use swaroop06/MLAgents-Pyramids with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use swaroop06/MLAgents-Pyramids with ml-agents:
mlagents-load-from-hf --repo-id="swaroop06/MLAgents-Pyramids" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
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Download README.md from swaroop06/MLAgents-Pyramids: direct link, hf CLI and curl.
- Browser
- Download file 868 Bytes
-
https://huggingface.co/swaroop06/MLAgents-Pyramids/resolve/main/README.md
- Command line
-
hf download hf://swaroop06/MLAgents-Pyramids/README.md
-
curl -L -o README.md https://huggingface.co/swaroop06/MLAgents-Pyramids/resolve/main/README.md
868 Bytes
metadata
library_name: ml-agents
tags:
- reinforcement-learning
- ML-Agents-Pyramids
- ml-agents
- unity-ml-agents
- deep-reinforcement-learning
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ML-Agents-Pyramids
type: ML-Agents-Pyramids
metrics:
- type: mean_reward
value: 1.85 +/- 0.30
name: mean_reward
PPO agent playing ML-Agents-Pyramids
This is a trained model of an PPO agent playing ML-Agents-Pyramids for the Hugging Face Deep Reinforcement Learning Course (Unit 5 P2).
Evaluation Results
- Mean Reward: 1.85 +/- 0.30
- Environment: ML-Agents-Pyramids
- Algorithm: PPO
- Library: ml-agents
Usage
Trained and evaluated for the Hugging Face Deep RL Course certification.