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arXiv Machine Learning By Felix St\"orck, Philipp Hartmann, Fabian Hinder, Klaus Neumann, Barbara Hammer

Space-sampled Value Decay: Forgetting Mechanisms for Non-stationary Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper introduces Space-sampled Value Decay (SsVD), a forgetting mechanism designed for non-stationary reinforcement learning where the environment can drift at every timestep. SsVD selectively pulls value estimates of randomly chosen state-space elements toward a baseline, discarding outdated information without requiring reset or change-point detection. Integrated into Soft Actor Critic and Deep Q-Networks, SsVD outperforms its base algorithms across six non-stationary environments and can also promote optimism in hard-exploration tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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