On the consistency of hyper-parameter selection in value-based deep reinforcement learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913590881026048 |
|---|---|
| author | Obando-Ceron, Johan Araújo, João G. M. Courville, Aaron Castro, Pablo Samuel |
| author_facet | Obando-Ceron, Johan Araújo, João G. M. Courville, Aaron Castro, Pablo Samuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17523 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the consistency of hyper-parameter selection in value-based deep reinforcement learning Obando-Ceron, Johan Araújo, João G. M. Courville, Aaron Castro, Pablo Samuel Machine Learning Artificial Intelligence 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. |
| title | On the consistency of hyper-parameter selection in value-based deep reinforcement learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.17523 |