让智能体根据环境变化动态调整目标优先级，实现更灵活的多目标规划。
Multi-Objective Planning with Contextual Lexicographic Reward Preferences
- 基于上下文动态决定目标优先级，支持不同情境下不同决策顺序。
- 通过专家轨迹推断状态与上下文映射，构建可循环的上下文感知策略。
- 适用于需要适应复杂场景的机器人、自动驾驶等系统。
自主智能体常需在多种目标之间进行规划，且目标的优先级会随上下文变化。在运行过程中，智能体可能遇到多个上下文，每个上下文对应不同的目标字典序，且关联不同的奖励函数。现有方法通常假设目标优先级在整个状态空间中保持不变，无法支持环境中多种优先级共存。本文提出上下文字典序马尔可夫决策过程（CLMDP），使目标排序和奖励函数由上下文决定。我们采用贝叶斯方法从专家轨迹中推断状态-上下文映射。求解算法先为每种优先级生成策略，再融合为单一上下文感知、无环的有效策略。实验在仿真和移动机器人上验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。