Reinforcement learning with thermal fluctuations at the nanoscale

Area of Science:

• Physics
• Chemistry
• Computer Science

Background:

• Reinforcement learning (RL) is a powerful control framework.
• Brownian fluctuations at the nanoscale limit precise control of nanomachines and molecular systems.

Purpose of the Study:

• To analyze nanoscale control limitations within the Markov decision process framework.
• To investigate the efficiency of reinforcement learning at the nanoscale.

Main Methods:

• Analysis using the general framework of Markov decision processes.
• Simulations of controlling small particle cluster shapes.

Main Results:

• Optimal nanoscale control improvement is proportional to (force * length) / temperature.
• Learned control improvement is proportional to the square of this ratio, reducing learning efficiency.
• Learning efficiency approaches zero at the nanoscale.

Conclusions:

• Nanoscale Brownian fluctuations significantly reduce reinforcement learning efficiency.
• Using actions learned at lower temperatures can circumvent these limitations.
• Effective nanoscale control strategies require accounting for thermal noise.