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New framework unifies reinforcement learning and stochastic processes

By PulseAugur Editorial · [1 sources] ·

A new paper introduces "action-driven processes" to unify perspectives from stochastic processes and reinforcement learning. This framework is applied to spiking neural networks, demonstrating that minimizing the Kullback-Leibler divergence between policy-driven and reward-driven distributions is equivalent to maximum entropy reinforcement learning. The research, authored by Shaowei Lin, was submitted to arXiv on October 30, 2025. AI

IMPACT Introduces a theoretical framework that could advance the understanding and application of reinforcement learning in complex systems.

RANK_REASON The cluster contains a single academic paper submission to arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework unifies reinforcement learning and stochastic processes

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The cluster contains a single academic paper submission to arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ruimin He, Shaowei Lin ·

    Action-Driven Processes for Continuous-Time Control

    arXiv:2510.26672v3 Announce Type: replace-cross Abstract: At the heart of reinforcement learning are actions -- decisions made in response to observations of the environment. Actions are equally fundamental in the modeling of stochastic processes, as they trigger discontinuous st…

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