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This arXiv paper argues that reinforcement-learning agents should eventually combine ten properties of continual learning within a single lifetime, rather than being evaluated on each in isolation. Its authors note that no published work has yet shown such integration, and they offer a playable persistent-world scenario and a proposed benchmark to test it. For ML readers, the useful point is the evaluation gap: strong results on separate benchmarks don't show that the capabilities coexist in one agent.
Continual Reinforcement Learning Is an Integration Problem
arXiv:2610.41809v1 Announce Type: new Abstract: This paper presents an opinionated set of ten desiderata intended to inform the design and evaluation of reinforcement-learning agents that learn continually throughout a continuing lifetime. We use these desiderata to examine a representative selection of existing approaches. Progress on individual desiderata is valuable, but the longer-term aim should be to integrate all ten within a single agent. To our knowledge, no published evaluation has yet provided evidence for such an integration. Likewise, the representative benchmarks we review probe different parts of this target, but separate evaluations do not establish that the corresponding properties coexist within one agent throughout a continuing lifetime. As a secondary contribution, we therefore present a compact persistent-world scenario as a playable companion to the desiderata and outline a proposed benchmark that examines a broad range of associated behavioral capabilities together within one such lifetime.
Abstract:This paper presents an opinionated set of ten desiderata intended to inform the design and evaluation of reinforcement-learning agents that learn continually throughout a continuing lifetime. We use these desiderata to examine a representative selection of existing approaches. Progress on individual desiderata is valuable, but the longer-term aim should be to integrate all ten within a single agent. To our knowledge, no published evaluation has yet provided evidence for such an integration. Likewise, the representative benchmarks we review probe different parts of this target, but separate evaluations do not establish that the corresponding properties coexist within one agent throughout a continuing lifetime. As a secondary contribution, we therefore present a compact persistent-world scenario as a playable companion to the desiderata and outline a proposed benchmark that examines a broad range of associated behavioral capabilities together within one such lifetime.
39 pages, 2 figures, 5 tables. Playable companion and source code: this https URL
Machine Learning (cs.LG)
arXiv:2610.41809 [cs.LG]
(or arXiv:2610.41809v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.41809 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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