Risk-aware Stochastic Shortest Path
This addresses risk management in decision-making processes for applications like robotics or finance, but it is incremental as it adapts existing risk measures to a known problem.
The paper tackled the problem of risk-aware control for stochastic shortest path on Markov decision processes by optimizing conditional value-at-risk instead of expectation, and introduced two algorithms that provide provably correct solutions, with evaluation showing feasibility on moderately sized models.
We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered for SSP, which however is oblivious to the incurred risk. We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. We treat both Markov chains as well as MDP and introduce, through novel insights, two algorithms, based on linear programming and value iteration, respectively. Both algorithms offer precise and provably correct solutions. Evaluation of our prototype implementation shows that risk-aware control is feasible on several moderately sized models.