AI RESEARCH
Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
arXiv CS.LG
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ArXi:2605.16318v1 Announce Type: new 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.