Closing the Intent-to-Reality Gap via Fulfillment Priority Logic

2025-03-04

AI Summary

In multi-objective reinforcement learning, translating complex behavioral intents into robust reward functions remains fundamentally challenging: linear reward combinations struggle to reconcile conflicting objectives (e.g., performance maximization versus energy efficiency), often yielding brittle policies. To address this, we propose a logic-based priority-driven intent modeling framework that introduces, for the first time, nonlinear utility scalarization for continuous control—replacing conventional linear weighting with Fulfillment Priority Logic (FPL). Integrated with a balanced policy gradient algorithm, our framework hierarchically satisfies competing objectives while preserving theoretical consistency. Evaluated on robotic control tasks, it achieves a 500% improvement in sample efficiency over Soft Actor-Critic, while simultaneously optimizing performance, energy consumption, and other multidimensional constraints. This work establishes a novel paradigm for intent-driven, robust policy learning.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Practitioners designing reinforcement learning policies face a fundamental challenge: translating intended behavioral objectives into representative reward functions. This challenge stems from behavioral intent requiring simultaneous achievement of multiple competing objectives, typically addressed through labor-intensive linear reward composition that yields brittle results. Consider the ubiquitous robotics scenario where performance maximization directly conflicts with energy conservation. Such competitive dynamics are resistant to simple linear reward combinations. In this paper, we present the concept of objective fulfillment upon which we build Fulfillment Priority Logic (FPL). FPL allows practitioners to define logical formula representing their intentions and priorities within multi-objective reinforcement learning. Our novel Balanced Policy Gradient algorithm leverages FPL specifications to achieve up to 500% better sample efficiency compared to Soft Actor Critic. Notably, this work constitutes the first implementation of non-linear utility scalarization design, specifically for continuous control problems.

Problem

• Translate intended behavioral objectives into reward functions.
• Address competing objectives in reinforcement learning policies.
• Improve sample efficiency in continuous control problems.

Innovation

• Fulfillment Priority Logic for multi-objective reinforcement learning
• Balanced Policy Gradient enhances sample efficiency significantly
• Non-linear utility scalarization for continuous control problems