arXiv:2608. 17749v1 Announce Type: new Abstract: Decentralised partially observable Markov decision processes (DecPOMDPs) provide a general framework for modelling multi-agent decision making under uncertainty.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
Decentralised partially observable Markov decision processes (DecPOMDPs) provide a general framework for modelling multi-agent decision making under uncertainty. However, DecPOMDPs are known to suffer from exponential complexity in the number of agents.
arXiv:2403. 19883v2 Announce Type: replace Abstract: Fully-observable non-deterministic (FOND) planning is at the core of artificial intelligence planning with uncertainty.
Robust Markov decision processes optimize one policy against a set of plausible transition functions. This can be conservative when the unknown dynamics are fixed and become partially identifiable after deployment.
arXiv:2608. 17929v1 Announce Type: new Abstract: Robust Markov decision processes optimize one policy against a set of plausible transition functions.
arXiv:2511. 19849v2 Announce Type: replace-cross Abstract: Recurrence objectives, where a target region must be visited infinitely often, are a fundamental class of specifications for Markov decision processes (MDPs) and form the core of $\omega$-regular and linear temporal logic (LTL) objectives.
arXiv:2601. 23229v2 Announce Type: replace Abstract: Markov decision processes (MDPs) are a fundamental model in sequential decision making.