On the consistency of hyper-parameter selection in value-based deep reinforcement learning
<|reference_start|>On the consistency of hyper-parameter selection in value-based deep reinforcement learning: 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.<|reference_end|>
@article{obando-ceron2024on,
    title={On the consistency of hyper-parameter selection in value-based deep
  reinforcement learning},
    author={Johan Obando-Ceron, Jo~ao G.M. Ara'ujo, Aaron Courville, Pablo
  Samuel Castro},
    journal={arXiv preprint arXiv:2406.17523},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17523},
    primaryClass={cs.LG cs.AI}
}