232 citations · 877 across the 30 of their papers we have counts for
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cs.LG2026
A perspective on fluid mechanical environments for challenges in reinforcement learning
Shruti Mishra, Michael Chang, Vamsi Spandan +1
We consider the challenge of developing agents that efficiently interact with high-dimensional, evolving environments, towards a view of practical reinforcement learning (RL) agent…
cs.LG2025
Harnessing intuitive local evolution rules for physical learning
Roie Ezraty, Menachem Stern, Shmuel M. Rubinstein
Machine Learning, however popular and accessible, is computationally intensive and highly power-consuming, prompting interest in alternative physical implementations of learning ta…