Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds

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.