On the consistency of hyper-parameter selection in value-based deep reinforcement learning

• URL: http://arxiv.org/abs/2406.17523v2
• Date: Tue, 2 Jul 2024 16:33:26 GMT
• Title: On the consistency of hyper-parameter selection in value-based deep reinforcement learning
• Authors: Johan Obando-Ceron, João G. M. Araújo, Aaron Courville, Pablo Samuel Castro,
• Abstract summary: This paper conducts an empirical study focusing on the reliability of hyper- parameter selection for value-based deep reinforcement learning agents. Our findings help establish which hyper- parameters are most critical to tune, and help clarify which tunings remain consistent across different training regimes.
• Score: 13.133865673667394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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