In this article, the authors introduce the concept of memory monoids as a unifying framework for efficient sequence modeling in recurrent reinforcement learning. They highlight the limitations of traditional approaches like segment-based batching (SBB) and propose Tape-Based Batching (TBB) combined with memory monoids to improve sample efficiency. The sensitivity analysis reveals that old observations significantly impact Q values, emphasizing the need for better generalization over time. Experiments show that TBB outperforms SBB in terms of sample efficiency across various tasks and models. The proposed resettable transformation prevents information leakage across episode boundaries, enhancing training efficiency. The study also evaluates the wall-clock efficiency of memory monoids, demonstrating significant speed-ups compared to standard methods. Overall, the research suggests that memory monoids coupled with TBB offer a promising approach to enhance efficiency and sample efficiency in recurrent reinforcement learning.
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by Steven Morad... klo arxiv.org 03-19-2024
https://arxiv.org/pdf/2402.09900.pdfSyvällisempiä Kysymyksiä