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This paper introduces Space-sampled Value Decay, a novel forgetting mechanism designed to help deep reinforcement learning agents adapt to environmental drift without relying on explicit task identifiers. By mimicking biological adaptation seen in rodents, the method offers a simpler alternative to existing non-stationary RL approaches that typically require perfect information about context changes. While the study demonstrates improved returns for DQN and SAC architectures in non-stationary settings, it also highlights specific limitations in performance that warrant further investigation.
Space-sampled Value Decay: Forgetting Mechanisms for Non-stationary Deep Reinforcement Learning
arXiv:2606.11797v1 Announce Type: new Abstract: Studies on rodents such as mice have shown the capabilities to adapt their behavior when dealing with changing parameters (``drift'') of the environment even if no information about change is provided (uncertainty) -- a behavior that can be modeled by forgetting mechanisms. Non-stationary Reinforcement Learning (NSRL) deals with adapting state-of-the-art RL methods to deal with changing environments: these however usually require (partially) perfect information about the drift such as ``task IDs'' or ``context''. To mitigate the effects of drift, this work develops \emph{Space-sampled Value Decay} as an explicit forgetting mechanism for value-based deep RL architectures as a simple yet effective approach. In particular we demonstrate and discuss positive effects but also limitations in achieved returns for modifications of Deep Q-networks (DQN) and Soft Actor-Critic (SAC) when evaluated on non-stationary environments.
Abstract:Studies on rodents such as mice have shown the capabilities to adapt their behavior when dealing with changing parameters (``drift'') of the environment even if no information about change is provided (uncertainty) -- a behavior that can be modeled by forgetting mechanisms. Non-stationary Reinforcement Learning (NSRL) deals with adapting state-of-the-art RL methods to deal with changing environments: these however usually require (partially) perfect information about the drift such as ``task IDs'' or ``context''. To mitigate the effects of drift, this work develops \emph{Space-sampled Value Decay} as an explicit forgetting mechanism for value-based deep RL architectures as a simple yet effective approach. In particular we demonstrate and discuss positive effects but also limitations in achieved returns for modifications of Deep Q-networks (DQN) and Soft Actor-Critic (SAC) when evaluated on non-stationary environments.
Machine Learning (cs.LG)
arXiv:2606.11797 [cs.LG]
(or arXiv:2606.11797v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.11797 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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