Papers › Recurrent Reinforcement Learning with Memoroids

Recurrent Reinforcement Learning with Memoroids

15 Feb 2024arXiv:2402.09900archive 2025-07-28

Steven Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki, Jakob Foerster, Amanda Prorok

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.

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1ran · honoured contract
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gamma_log_init proroklab/memory-monoids/memory/lru.py official repository ran · fixture could not drive it fingerprinted no licence file found · pointer only · f1d67707c6650049 · report
matrix_init proroklab/memory-monoids/memory/lru.py official repository ran · our draft was wrong no licence file found · pointer only · 702f95276a6ff829 · report
nu_init proroklab/memory-monoids/memory/lru.py official repository ran · our draft was wrong no licence file found · pointer only · f832dda1d4b9b1d8 · report
theta_init proroklab/memory-monoids/memory/lru.py official repository ran · our draft was wrong no licence file found · pointer only · 5eb92a40d3fbcc07 · report
wrapped_associative_update proroklab/memory-monoids/memory/lru.py official repository ran · our draft was wrong no licence file found · pointer only · be06105188b4bb18 · report
LRU proroklab/memory-monoids/memory/lru.py official repository unverified no licence file found · pointer only · 871a68de4ff3cdef · report
binary_operator_diag identical code first harvested elsewhere ran · honoured contract licence of this copy not recorded · 0212f0ba3cfe6d69 · report

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Reinforcement Learningreinforcement-learning

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