The paper investigates whether a frozen recurrent network can provide sufficient memory for partially observable Markov decision processes (POMDPs). Using a fixed recurrent network combined with a trainable output module, the authors demonstrate that such networks can recall information over hundreds of steps and achieve competitive control performance across diverse POMDP tasks, while using fewer than half the trainable parameters of fully trainable recurrent networks. These findings suggest that learning the recurrent memory module is not always necessary, offering a simple, parameter‑efficient baseline for partially observable reinforcement learning.
By Zekang Wang, Zhe He, Gaojie Jin
arXiv:2609.38598v1 Announce Type: new
Abstract: Partially observable environments pose a fundamental challenge in deep reinforcement learning, requiring agents to compress temporal information from o...
By Sathya Kamesh Bhethanabhotla, Efstratios Gavves, Andr\'e Biedenkapp
arXiv:2605. 24709v2 Announce Type: replace Abstract: Streaming reinforcement learning has emerged as an online learning paradigm that conforms to the restrictions of natural learning agents that process data incrementally, i.
By Noah Farr, Aryaman Reddi, Carlo D'Eramo, Jan Peters
The paper introduces memoroids, a monoid‑based framework that reformulates memory models like RNNs and Transformers for better handling of long sequences in reinforcement learning. It critiques traditional batching approaches in recurrent RL, identifies their shortcomings, and proposes a new batching method using memoroids that enhances sample efficiency, boosts return, and simplifies recurrent loss implementation.
By Steven Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki, Jakob Foerster, Amanda Prorok
The paper introduces ELiSe, a model that leverages cortical network scaffolds and dendritic compartments to learn complex non‑Markovian spatio‑temporal patterns using only local, always‑on, phase‑free synaptic plasticity. It demonstrates the model’s ability to acquire and replay intricate sequences, exemplified by a birdsong learning mock‑up, and shows robustness to external disturbances and flexibility in parameter settings.
By Laura Kriener, Kristin V\"olk, Ben von H\"unerbein, Federico Benitez, Walter Senn, Mihai A. Petrovici
arXiv:2603. 23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting where the Bellman backup of any linear value function remains linear.
By Zakaria Mhammedi, Alexander Rakhlin, Nneka Okolo