2023/11/29 by Francesco Boccardo, Boccardo, Francesco, Olivier Pierre-Louis +1
Biochemistry, Genetics and Molecular Biology · Engineering · Physics and Astronomy · #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Force Microscopy Techniques and Applications #Lipid Membrane Structure and Behavior #Molecular Junctions and Nanostructures #Statistical Mechanics (cond-mat.stat-mech)
paper · pdf · doi:10.48550/arxiv.2311.17519
openalex publication_date 2023/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Reinforcement Learning offers a framework to learn to choose actions in order to achieve some goal. However, at the nano-scale, thermal fluctuations hamper the learning process. We analyze this regime using the general framework of Markov Decision Processes, which applies to a wide variety of problems from nano-navigation to nano-machine actuation. We show that at the nan-oscale, while optimal actions should bring an improvement proportional to the small ratio of the applied force times a length-scale over the temperature, the learned improvement is smaller and proportional to the square of this small ratio. Consequently, the efficiency of learning, which compares the learning improvement to the theoretical optimal improvement, drops to zero. Nevertheless, these limitations can be circumvented by using actions learned at a lower temperature. These results are illustrated with simulations of the control of the shape of small particle clusters.