Reinforcement Learning for Ising Model

The Ising Spin Glasses model, a fundamental concept in statistical mechanics and condensed matter physics, provides insights into the behavior of interacting spins within a physical system, while also being able to formulate many combinatorial optimization problems. However, solving the Ising Model for large and complex systems is computationally demanding and often infeasible using traditional methods. In this paper, we present the deterministic REINFORCE algorithm tailored for the Ising Model, enabling state-of-the-art performance through learned state transition policies. In our work, we first formulate the Ising Model with MaxCut Problem as a case study. Secondly, we propose a novel deterministic REINFORCE algorithm incorporating the Local Search approach. Finally, we evaluate our algorithm on well-known datasets and demonstrate state-of-the-art performance.