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We show how to calculate the likelihood of dynamical large deviations usingevolutionary reinforcement learning. An agent, a stochastic model, propagates acontinuous-time Monte Carlo trajectory and receives a reward conditioned uponthe values of certain path-extensive quantities. Evolution producesprogressively fitter agents, eventually allowing the calculation of a piece ofa large-deviation rate function for a particular model and path-extensivequantity. For models with small state spaces the evolutionary process actsdirectly on rates, and for models with large state spaces the process acts onthe weights of a neural network that parameterizes the model's rates. Thisapproach shows how path-extensive physics problems can be considered within aframework widely used in machine learning.

Evolutionary reinforcement learning of dynamical large deviations