This page is dedicated to interesting papers in on Large Deviation Theory (LDT). Bibliography Tool

- S. Whitelam, D. Jacobson, I. Tamblyn. Evolutionary reinforcement learning of dynamical large deviations. J. Chem. Phys. 153, 044113, 2020. Abstract.
We show how to bound and calculate the likelihood of dynamical large deviations using evolutionary reinforcement learning. An agent, a stochastic model, propagates a continuous-time Monte Carlo trajectory and receives a reward conditioned upon the values of certain path-extensive quantities. Evolution produces progressively fitter agents, potentially allowing the calculation of a piece of a large-deviation rate function for a particular model and path-extensive quantity. For models with small state spaces, the evolutionary process acts directly on rates, and for models with large state spaces, the process acts on the weights of a neural network that parameterizes the modelâ€™s rates. This approach shows how path-extensive physics problems can be considered within a framework widely used in machine learning.