Deep reinforcement learning (deep RL) has achieved tremendous success on various domains through a combination of algorithmic design and careful selection of hyper-parameters. Algorithmic improvements are often the result of iterative enhancements built upon prior approaches, while hyper-parameter choices are typically inherited from previous methods or fine-tuned specifically for the proposed technique. Despite their crucial impact on performance, hyper-parameter choices are frequently overshadowed by algorithmic advancements. This paper conducts an extensive empirical study focusing on the reliability of hyper-parameter selection for value-based deep reinforcement learning agents, including the introduction of a new score to quantify the consistency and reliability of various hyper-parameters. Our findings not only help establish which hyper-parameters are most critical to tune, but also help clarify which tunings remain consistent across different training regimes.
@article{arxiv.2406.17523,
title = {On the consistency of hyper-parameter selection in value-based deep reinforcement learning},
author = {Johan Obando-Ceron and João G. M. Araújo and Aaron Courville and Pablo Samuel Castro},
journal= {arXiv preprint arXiv:2406.17523},
year = {2024}
}