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
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Download README.md from edbeeching/atari_2B_atari_choppercommand_2222: direct link, hf CLI and curl.
- Browser
- Download file 636 Bytes
-
https://huggingface.co/edbeeching/atari_2B_atari_choppercommand_2222/resolve/main/README.md
- Command line
-
hf download hf://edbeeching/atari_2B_atari_choppercommand_2222/README.md
-
curl -L -o README.md https://huggingface.co/edbeeching/atari_2B_atari_choppercommand_2222/resolve/main/README.md
636 Bytes
metadata
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