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New RL framework tackles stochastic control problems with unknown parameters

By PulseAugur Editorial · [1 sources] ·

This paper introduces a new reinforcement learning framework for solving complex stochastic control problems where key parameters like drift coefficients and running reward functions are unknown. The proposed algorithms aim to learn optimal value functions and feedback control policies, with theoretical grounding and demonstrated convergence. The research also explores specific cases involving control-dependent diffusion, requiring advanced probabilistic representations. AI

IMPACT Introduces novel algorithms for solving complex stochastic control problems, potentially advancing research in areas requiring adaptive decision-making under uncertainty.

RANK_REASON The item is an academic paper published on arXiv detailing new theoretical algorithms and convergence proofs for a specific class of control problems. [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 RL framework tackles stochastic control problems with unknown parameters

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The item is an academic paper published on arXiv detailing new theoretical algorithms and convergence proofs for a specific class of control problems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jin Ma, Gaozhan Wang, Jianfeng Zhang, Xunyu Zhou ·

    Learning to Solve Stochastic Controls with Unknown Drifts and Running Rewards: Theory, Algorithms and Convergence

    arXiv:2609.14972v1 Announce Type: new Abstract: We study continuous-time and possibly high-dimensional stochastic control problems where drift coefficients and running reward functions are unknown. Due to these missing model primitives, we take the exploratory, reinforcement lear…

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