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

Bassel El Mabsout, Abdelrahman Abdelgawad, Renato Mancuso

TL;DR

This work tackles the challenge of translating multi-objective intentions into effective rewards by introducing Fulfillment Priority Logic (FPL) and the Balanced Policy Gradient (BPG) algorithm. FPL formalizes priority-aware objective composition using power means to preserve intended trade-offs, enabling non-linear utility designs in continuous control. BPG optimizes Fulfillment Q-values under FPL, incorporating a conservative fulfillment-based regularization to mitigate overestimation and achieve substantial improvements in sample efficiency on standard continuous-control benchmarks. The empirical results show strong gains in data efficiency and clearer, more desirable agent behaviors compared to traditional reward engineering and baseline methods, suggesting that FPL provides a principled pathway to closer alignment between practitioner intent and learned behavior in multi-objective RL.

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.

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

TL;DR

This work tackles the challenge of translating multi-objective intentions into effective rewards by introducing Fulfillment Priority Logic (FPL) and the Balanced Policy Gradient (BPG) algorithm. FPL formalizes priority-aware objective composition using power means to preserve intended trade-offs, enabling non-linear utility designs in continuous control. BPG optimizes Fulfillment Q-values under FPL, incorporating a conservative fulfillment-based regularization to mitigate overestimation and achieve substantial improvements in sample efficiency on standard continuous-control benchmarks. The empirical results show strong gains in data efficiency and clearer, more desirable agent behaviors compared to traditional reward engineering and baseline methods, suggesting that FPL provides a principled pathway to closer alignment between practitioner intent and learned behavior in multi-objective RL.

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.

Paper Structure

This paper contains 34 sections, 4 theorems, 18 equations, 2 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Reward Design
  4. Multi-Objective RL
  5. Formal Methods
  6. The Sample Efficiency Challenge
  7. Definitions
  8. Multi-Objective Markov Decision Processes (MO-MDPs)
  9. Fulfillment
  10. Multi-Fulfillment MDPs (MF-MDPs)
  11. Fulfillment Q-values (FQ-values)
  12. Fulfillment Priority Logic
  13. Power Mean Foundations
  14. Logical Language
  15. Relation to Fuzzy Logic
  16. ...and 19 more sections

Key Result

Theorem IV.1

This bound guarantees that when a power mean outputs value $y$, every input component must have at least fulfillment $\sqrt[^p]{n(y^p - 1) + 1}$.

Figures (2)

  • Figure 1: The top figures show violin plots indicating the distribution of timesteps required to reach performance thresholds accross 10 random seeds. The red horizontal line separates seeds failing to reach the threshold. In the bottom figures, we show a smoothened training progress of rewards versus environment steps for each algorithm. Shaded regions represent standard deviation accross seeds, and the dashed lines indicate the placement of reward thresholds for each environment.
  • Figure : Balanced Policy Gradient (BPG)

Theorems & Definitions (8)

  • Theorem IV.1: Minimum Fulfillment Bound
  • proof
  • Theorem IV.2: Power Mean Conservation
  • proof
  • Lemma IV.3: Worst Case Configuration
  • proof
  • Lemma IV.4: Explicit Minimum Solution
  • proof