Recurrent Reinforcement Learning with Memoroids
Quick summary
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 appro
Key takeaways
- 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.
Why it matters
“Recurrent Reinforcement Learning with Memoroids” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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