Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
ChopperCommandNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_choppercommand_2222 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use edbeeching/atari_2B_atari_choppercommand_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_choppercommand_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
File size: 636 Bytes
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library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
- ChopperCommandNoFrameskip-v4
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_choppercommand
type: atari_choppercommand
metrics:
- type: mean_reward
value: 61585.00 +/- 214116.50
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_choppercommand** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
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