  We show how to 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, eventually 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 model's rates. This
approach shows how path-extensive physics problems can be considered within a
framework widely used in machine learning.
