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New Heavy-Ball Q-Learning method promises faster reinforcement learning convergence

By PulseAugur Editorial · [3 sources] ·

Researchers have introduced a novel Heavy-Ball Q-Learning method designed to enhance reinforcement learning algorithms. This new approach establishes convergence guarantees and identifies conditions under which it can theoretically achieve faster convergence than standard Q-learning. The method's effectiveness is further demonstrated through its extension to Q-learning with linear function approximation, yielding similar convergence and acceleration results. AI

IMPACT Introduces a theoretical advancement in reinforcement learning algorithms, potentially leading to more efficient training of AI agents.

RANK_REASON The cluster contains two identical arXiv submissions of a research paper detailing a new algorithm.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New Heavy-Ball Q-Learning method promises faster reinforcement learning convergence

COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Donghwan Lee ·

    Heavy-Ball Q-Learning with Residual Weighting Correction

    arXiv:2606.27112v1 Announce Type: cross Abstract: This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence. It also identifies conditions under which the method is theoretically guaranteed to converge faster than…

  2. arXiv cs.LG TIER_1 English(EN) · Donghwan Lee ·

    Heavy-Ball Q-Learning with Residual Weighting Correction

    This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence. It also identifies conditions under which the method is theoretically guaranteed to converge faster than standard Q-learning. The same construction is the…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Heavy-Ball Q-Learning with Residual Weighting Correction

    This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence. It also identifies conditions under which the method is theoretically guaranteed to converge faster than standard Q-learning. The same construction is the…

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