Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures
Umit Köse, Andrzej Ruszczyński; 22(38):1−34, 2021.
We propose a novel reinforcement learning methodology where the system performance is evaluated by a Markov coherent dynamic risk measure with the use of linear value function approximations. We construct projected risk-averse dynamic programming equations and study their properties. We propose new risk-averse counterparts of the basic and multi-step methods of temporal differences and we prove their convergence with probability one. We also perform an empirical study on a complex transportation problem.
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