Multi-Objective Planning with Contextual Lexicographic Reward Preferences
This addresses the challenge for autonomous systems that need to adapt to different contexts with distinct objective priorities, though it is incremental as it builds on existing multi-objective planning methods.
The paper tackled the problem of autonomous agents planning under multiple objectives with varying preference orderings based on context, by introducing the Contextual Lexicographic Markov Decision Process (CLMDP) framework and an algorithm that computes and combines policies for each ordering, with evaluation in simulation and on a mobile robot.
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.