Dynamic Programming in Distributional Reinforcement Learning

Last summer, I supervised a summer intern, Cédric Odin, student at Ecole Normale in Ker Lann, France, on Dynamic Programming in Distributional Reinforcement Learning. A state-of-the-art is now available online https://hal.archives-ouvertes.fr/hal-03168889 The classic approach to reinforcement learning is limited in that it only predicts the expected return. The specialized literature has long tried to remedy this problem by studying risk-sensitive models, but the distributional approach will not emerge until 2017. Since the seminal article M. G. Bellemare, Dabney, and Munos 2017 and the …