Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning

TL;DR

This paper presents a reinforcement learning framework that autonomously discovers critical parameters in the Ising model, outperforming traditional methods and mimicking phase transition behaviors.

Contribution

It introduces a physics-inspired reinforcement learning approach that autonomously identifies critical parameters in the Ising model, enhancing interpretability and efficiency.

Findings

Outperforms traditional methods in accuracy and robustness

Exhibits phase transition-like search behavior

Effectively handles environments with strong perturbations

Abstract

Traditional methods for determining critical parameters are often influenced by human factors. This research introduces a physics-inspired adaptive reinforcement learning framework that enables agents to autonomously interact with physical environments, simultaneously identifying both the critical temperature and various types of critical exponents in the Ising model with precision. Interestingly, our algorithm exhibits search behavior reminiscent of phase transitions, efficiently converging to target parameters regardless of initial conditions. Experimental results demonstrate that this method significantly outperforms traditional approaches, particularly in environments with strong perturbations. This study not only incorporates physical concepts into machine learning to enhance algorithm interpretability but also establishes a new paradigm for scientific exploration, transitioning from manual analysis to autonomous AI discovery.