このアルゴリズムがロボットのパフォーマンスを向上させる(This algorithm makes robots perform better)

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2024-05-02 ノースウェスタン大学

ノースウェスタン大学のエンジニアが、スマートロボティクス向けの新しいAIアルゴリズム「MaxDiff RL」を開発しました。このアルゴリズムは、ロボットがランダムに環境を探索することで多様な経験を得る能力を強化し、効率的な学習と信頼性の向上を可能にします。実験では、他の最先端のAIモデルに対して一貫して優れた性能を示しました。アルゴリズムの特性から、さまざまなロボティクス分野での応用が期待されます。

<関連情報>

最大拡散強化学習 Maximum diffusion reinforcement learning

Thomas A. Berrueta,Allison Pinosky & Todd D. Murphey
Nature Machine Intelligence  Published:02 May 2024
DOI:https://doi.org/10.1038/s42256-024-00829-3

extended data figure 1

Abstract

Robots and animals both experience the world through their bodies and senses. Their embodiment constrains their experiences, ensuring that they unfold continuously in space and time. As a result, the experiences of embodied agents are intrinsically correlated. Correlations create fundamental challenges for machine learning, as most techniques rely on the assumption that data are independent and identically distributed. In reinforcement learning, where data are directly collected from an agent’s sequential experiences, violations of this assumption are often unavoidable. Here we derive a method that overcomes this issue by exploiting the statistical mechanics of ergodic processes, which we term maximum diffusion reinforcement learning. By decorrelating agent experiences, our approach provably enables single-shot learning in continuous deployments over the course of individual task attempts. Moreover, we prove our approach generalizes well-known maximum entropy techniques and robustly exceeds state-of-the-art performance across popular benchmarks. Our results at the nexus of physics, learning and control form a foundation for transparent and reliable decision-making in embodied reinforcement learning agents.

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