Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Teodor Moldovan, Pieter Abbeel


The expected return is a widely used objective in decision making under uncer- tainty. Many algorithms, such as value iteration, have been proposed to optimize it. In risk-aware settings, however, the expected return is often not an appropriate objective to optimize. We propose a new optimization objective for risk-aware planning and show that it has desirable theoretical properties. We also draw con- nections to previously proposed objectives for risk-aware planing: minmax, ex- ponential utility, percentile and mean minus variance. Our method applies to an extended class of Markov decision processes: we allow costs to be stochastic as long as they are bounded. Additionally, we present an ef´Čücient algorithm for op- timizing the proposed objective. Synthetic and real-world experiments illustrate the effectiveness of our method, at scale.