aiqbench analysis
This research investigates Evolution Strategies (ES) within continual control frameworks, specifically addressing catastrophic forgetting during sequential MuJoCo locomotion tasks. The study demonstrates that replay mechanisms significantly enhance retention and facilitate positive transfer between changing environments without compromising prior knowledge. These findings advance the field of machine learning by establishing effective strategies for maintaining plasticity in adaptive agents facing dynamic task sequences.
Continual Evolution Strategies in Control Tasks
arXiv:2608.13600v1 Announce Type: cross Abstract: We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without forgetting previous ones. On sequential MuJoCo locomotion tasks, naive ES suffers from severe catastrophic forgetting. Replay substantially improves retention and can induce positive transfer, while larger replay budgets reduce plasticity. Overall, these results show that ES can support continual adaptation in control and that replay is an effective mechanism for mitigating forgetting.
Abstract:We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without forgetting previous ones. On sequential MuJoCo locomotion tasks, naive ES suffers from severe catastrophic forgetting. Replay substantially improves retention and can induce positive transfer, while larger replay budgets reduce plasticity. Overall, these results show that ES can support continual adaptation in control and that replay is an effective mechanism for mitigating forgetting.
Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
arXiv:2608.13600 [cs.NE]
(or arXiv:2608.13600v1 [cs.NE] for this version)
https://doi.org/10.48550/arXiv.2608.13600 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 165 words
