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This research introduces "memoroids," a monoid-based framework that reformulates recurrent neural networks and transformers to better handle long sequences in partially observable environments. By addressing theoretical limitations in traditional batching for reinforcement learning, the proposed method demonstrably improves sample efficiency and return while simplifying loss function implementations. The work represents a significant advancement in memory modeling architectures essential for scaling AI agents on complex tasks.
Recurrent Reinforcement Learning with Memoroids
arXiv:2402.09900v4 Announce Type: replace-cross Abstract: Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov states. Neither model scales particularly well to long sequences, especially compared to an emerging class of memory models called Linear Recurrent Models. We discover that the recurrent update of these models resembles a monoid, leading us to reformulate existing models using a novel monoid-based framework that we call memoroids. We revisit the traditional approach to batching in recurrent reinforcement learning, highlighting theoretical and empirical deficiencies. We leverage memoroids to propose a batching method that improves sample efficiency, increases the return, and simplifies the implementation of recurrent loss functions in reinforcement learning.
Abstract:Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov states. Neither model scales particularly well to long sequences, especially compared to an emerging class of memory models called Linear Recurrent Models. We discover that the recurrent update of these models resembles a monoid, leading us to reformulate existing models using a novel monoid-based framework that we call memoroids. We revisit the traditional approach to batching in recurrent reinforcement learning, highlighting theoretical and empirical deficiencies. We leverage memoroids to propose a batching method that improves sample efficiency, increases the return, and simplifies the implementation of recurrent loss functions in reinforcement learning.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2402.09900 [cs.LG]
(or arXiv:2402.09900v4 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2402.09900 Focus to learn more arXiv-issued DOI via DataCite
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