Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
This paper systematically investigates how action information can be incorporated into the state update function of recurrent neural networks (RNNs) in reinforcement learning (RL). The authors discuss multiple encoding choices, provide empirical evaluations across illustrative domains, and outline future work and challenges specific to RL.
[2605.16318] Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
[Submitted on 4 May 2026]
Title:Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
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Abstract:Building and maintaining state to learn policies and value functions is critical for deploying reinforcement learning (RL) agents in the real world. Recurrent neural networks (RNNs) have become a key point of interest for the state-building problem, and several large-scale reinforcement learning agents incorporate recurrent networks. While RNNs have become a mainstay in many RL applications, many key design choices and implementation details responsible for performance improvements are often not reported. In this work, we discuss one axis on which RNN architectures can be (and have been) modified for use in RL. Specifically, we look at how action information can be incorporated into the state update function of a recurrent cell. We discuss several choices in using action information and empirically evaluate the resulting architectures on a set of illustrative domains. Finally, we discuss future work in developing recurrent cells and discuss challenges specific to the RL setting.
Comments: Published in TMLR in 2023, https: // openreview. net/ forum? id= K6g4MbAC1r .Transactions on Machine Learning Research (2023)
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2605.16318 [cs.LG]
(or arXiv:2605.16318v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.16318
arXiv-issued DOI via DataCite
Submission history
From: Matthew Schlegel [view email] [v1] Mon, 4 May 2026 22:18:05 UTC (14,036 KB)
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