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Risk-Averse Learning by Temporal Difference Methods
Published 2 Mar 2020 in math.OC and cs.LG | (2003.00780v1)
Abstract: We consider reinforcement learning with performance evaluated by a dynamic risk measure. We construct a projected risk-averse dynamic programming equation and study its properties. Then we propose risk-averse counterparts of the 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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