Reinforcement Learning
ml-agents
TensorBoard
ONNX
ML-Agents-Pyramids
ppo
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use SnEhAh018/ppo-PyramidsFast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use SnEhAh018/ppo-PyramidsFast with ml-agents:
mlagents-load-from-hf --repo-id="SnEhAh018/ppo-PyramidsFast" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
|
Download README.md from SnEhAh018/ppo-PyramidsFast: direct link, hf CLI and curl.
- Browser
- Download file 828 Bytes
-
https://huggingface.co/SnEhAh018/ppo-PyramidsFast/resolve/main/README.md
- Command line
-
hf download hf://SnEhAh018/ppo-PyramidsFast/README.md
-
curl -L -o README.md https://huggingface.co/SnEhAh018/ppo-PyramidsFast/resolve/main/README.md
828 Bytes
metadata
tags:
- ML-Agents-Pyramids
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- ml-agents
library_name: ml-agents
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: -100
name: mean_reward
verified: false
PPO Agent playing ML-Agents-Pyramids
This is a trained PPO agent playing the ML-Agents-Pyramids environment using Unity ML-Agents.
This model was trained as part of the Hugging Face Deep Reinforcement Learning Course, Unit 5.
Environment
ML-Agents-Pyramids
Algorithm
Proximal Policy Optimization (PPO)
Training
The model was trained using Unity ML-Agents and exported to ONNX format.