Publication details
Optimizing Expectation with Guarantees in POMDPs
| Authors | |
|---|---|
| Year of publication | 2017 |
| Type | Article in Proceedings |
| Conference | Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI) |
| Citation | |
| Web | http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14354 |
| Keywords | Partially-observable Markov decision processes; Discounted payoff; Probabilistic planning; Verification |
| Description | A standard objective in partially-observable Markov decision processes (POMDPs) is to find a policy that maximizes the expected discounted-sum payoff. However, such policies may still permit unlikely but highly undesirable outcomes, which is problematic especially in safety-critical applications. Recently, there has been a surge of interest in POMDPs where the goal is to maximize the probability to ensure that the payoff is at least a given threshold, but these approaches do not consider any optimization beyond satisfying this threshold constraint. In this work we go beyond both the “expectation” and “threshold” approaches and consider a “guaranteed payoff optimization (GPO)” problem for POMDPs, where we are given a threshold t and the objective is to find a policy ? such that a) each possible outcome of ? yields a discounted-sum payoff of at least t, and b) the expected discounted-sum payoff of ? is optimal (or near-optimal) among all policies satisfying a). We present a practical approach to tackle the GPO problem and evaluate it on standard POMDP benchmarks. |