Optimal Control of Logically Constrained Partially Observable and Multi-Agent Markov Decision Processes
This work addresses the challenge of ensuring safety and operational requirements in complex autonomous systems, representing an incremental advancement by building on existing POMDP and multi-agent control methods.
The paper tackles the problem of controlling autonomous systems with logical constraints under partial observability and multi-agent settings, introducing an optimal control theory for POMDPs with temporal logic constraints and extending it to multi-agent scenarios with guarantees on reward optimality and constraint satisfaction.
Autonomous systems often have logical constraints arising, for example, from safety, operational, or regulatory requirements. Such constraints can be expressed using temporal logic specifications. The system state is often partially observable. Moreover, it could encompass a team of multiple agents with a common objective but disparate information structures and constraints. In this paper, we first introduce an optimal control theory for partially observable Markov decision processes (POMDPs) with finite linear temporal logic constraints. We provide a structured methodology for synthesizing policies that maximize a cumulative reward while ensuring that the probability of satisfying a temporal logic constraint is sufficiently high. Our approach comes with guarantees on approximate reward optimality and constraint satisfaction. We then build on this approach to design an optimal control framework for logically constrained multi-agent settings with information asymmetry. We illustrate the effectiveness of our approach by implementing it on several case studies.