NeurIPS 2023poster18 citations
Small batch deep reinforcement learning
Johan Samir Obando Ceron, Marc G Bellemare, Pablo Samuel Castro
Abstract
In value-based deep reinforcement learning with replay memories, the batch size parameter specifies how many transitions to sample for each gradient update. Although critical to the learning process, this value is typically not adjusted when proposing new algorithms. In this work we present a broad empirical study that suggests reducing the batch size can result in a number of significant performance gains; this is surprising, as the general tendency when training neural networks is towards larger batch sizes for improved performance. We complement our experimental findings with a set of empirical analyses towards better understanding this phenomenon.
Reinforcement LearningDeep Reinforcement LearningValue basedBatch Size
BibTeX
@inproceedings{
ceron2023small,
title={Small batch deep reinforcement learning},
author={Johan Samir Obando Ceron and Marc G Bellemare and Pablo Samuel Castro},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=wPqEvmwFEh}
}