Stochastically Dominant Distributional Reinforcement Learning [article]

John D. Martin, Michal Lyskawinski, Xiaohu Li, Brendan Englot
2020 arXiv   pre-print
We describe a new approach for managing aleatoric uncertainty in the Reinforcement Learning (RL) paradigm. Instead of selecting actions according to a single statistic, we propose a distributional method based on the second-order stochastic dominance (SSD) relation. This compares the inherent dispersion of random returns induced by actions, producing a more comprehensive and robust evaluation of the environment's uncertainty. The necessary conditions for SSD require estimators to predict
more » ... e second moments. To accommodate this, we map the distributional RL problem to a Wasserstein gradient flow, treating the distributional Bellman residual as a potential energy functional. We propose a particle-based algorithm for which we prove optimality and convergence. Our experiments characterize the algorithm performance and demonstrate how uncertainty and performance are better balanced using an ssd policy than with other risk measures.
arXiv:1905.07318v4 fatcat:4m4ps6suk5cy5nnhngr7a4ioga