Multi-Objective Planning with Contextual Lexicographic Reward Preferences

TL;DR

This paper introduces CLMDP, a framework enabling autonomous agents to plan with varying lexicographic preferences over multiple objectives depending on context, addressing limitations of existing single-preference planning methods.

Contribution

The paper proposes CLMDP, a novel framework for multi-objective planning with context-dependent lexicographic preferences, including a Bayesian inference method and a combined policy algorithm.

Findings

Effective in simulation environments

Successfully applied to mobile robot planning

Supports multiple context-specific preferences

Abstract

Autonomous agents are often required to plan under multiple objectives whose preference ordering varies based on context. The agent may encounter multiple contexts during its course of operation, each imposing a distinct lexicographic ordering over the objectives, with potentially different reward functions associated with each context. Existing approaches to multi-objective planning typically consider a single preference ordering over the objectives, across the state space, and do not support planning under multiple objective orderings within an environment. We present Contextual Lexicographic Markov Decision Process (CLMDP), a framework that enables planning under varying lexicographic objective orderings, depending on the context. In a CLMDP, both the objective ordering at a state and the associated reward functions are determined by the context. We employ a Bayesian approach to infer a state-context mapping from expert trajectories. Our algorithm to solve a CLMDP first computes a policy for each objective ordering and then combines them into a single context-aware policy that is valid and cycle-free. The effectiveness of the proposed approach is evaluated in simulation and using a mobile robot.