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arXiv · 2007.00085

Enforcing Almost-Sure Reachability in POMDPs

Sebastian Junges·Nils Jansen·Sanjit A. Seshia
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Abstract

Partially-Observable Markov Decision Processes (POMDPs) are a well-known stochastic model for sequential decision making under limited information. We consider the EXPTIME-hard problem of synthesising policies that almost-surely reach some goal state without ever visiting a bad state. In particular, we are interested in computing the winning region, that is, the set of system configurations from which a policy exists that satisfies the reachability specification. A direct application of such a winning region is the safe exploration of POMDPs by, for instance, restricting the behavior of a reinforcement learning agent to the region. We present two algorithms: A novel SAT-based iterative approach and a decision-diagram based alternative. The empirical evaluation demonstrates the feasibility and efficacy of the approaches.

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Sebastian Junges, Nils Jansen, Sanjit A. Seshia. 2020-06-30. Enforcing Almost-Sure Reachability in POMDPs. https://arxiv.org/abs/2007.00085

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