# Evolutionary reinforcement learning of dynamical large deviations

arXiv: https://arxiv.org/abs/1909.00835 · HTML: https://mlsanity.com/paper/1909.00835/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Reinforcement learning of rare diffusive dynamics](https://mlsanity.com/paper/2105.04321/) (2021)
- [Accelerating GMRES with Deep Learning in Real-Time](https://mlsanity.com/paper/2103.10975/) (2021)
- [Entropy Regularized Reinforcement Learning Using Large Deviation Theory](https://mlsanity.com/paper/2106.03931/) (2021)
- [Learning stochastic dynamics and predicting emergent behavior using transformers](https://mlsanity.com/paper/2202.08708/) (2022)
- [Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations](https://mlsanity.com/paper/2209.14089/) (2022)

## Abstract (arXiv)

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.