Optimal Control of Partially Observable Markov Decision Processes with Finite Linear Temporal Logic Constraints

TL;DR

This paper introduces a novel framework for designing policies for partially observable agents that maximize reward while satisfying complex temporal logic constraints, leveraging off-the-shelf POMDP solvers.

Contribution

It formulates the problem as a constrained POMDP with finite linear temporal logic specifications and provides a method to solve it using existing unconstrained POMDP algorithms.

Findings

Effective policy synthesis for POMDPs with temporal logic constraints.

Guarantees approximate optimality and high-probability constraint satisfaction.

Validated on multiple models demonstrating practical applicability.

Abstract

Autonomous agents often operate in scenarios where the state is partially observed. In addition to maximizing their cumulative reward, agents must execute complex tasks with rich temporal and logical structures. These tasks can be expressed using temporal logic languages like finite linear temporal logic (LTL_f). This paper, for the first time, provides a structured framework for designing agent policies that maximize the reward while ensuring that the probability of satisfying the temporal logic specification is sufficiently high. We reformulate the problem as a constrained partially observable Markov decision process (POMDP) and provide a novel approach that can leverage off-the-shelf unconstrained POMDP solvers for solving it. Our approach guarantees approximate optimality and constraint satisfaction with high probability. We demonstrate its effectiveness by implementing it on several models of interest.