Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners

2025-03-08

AI Summary

Designing and evaluating reward functions in reinforcement learning (RL) remains challenging, often limiting agent performance. This paper formally defines the “reward alignment” problem—quantifying how accurately a reward function reflects human preferences. To address it, we propose the Trajectory Alignment Coefficient (TAC): a principled, quantitative alignment metric that requires no ground-truth rewards, is invariant to potential-based reward shaping, and is compatible with online RL. TAC integrates preference modeling with trajectory distribution similarity and incorporates statistical significance testing. Validated via a user study with 11 RL practitioners, TAC reduces cognitive load by 1.5×, is preferred by 82% of participants, and improves reward selection success rate by 41%. This work establishes the first truth-free, interpretable, and deployable alignment evaluation framework for reward engineering.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Reinforcement learning agents are fundamentally limited by the quality of the reward functions they learn from, yet reward design is often overlooked under the assumption that a well-defined reward is readily available. However, in practice, designing rewards is difficult, and even when specified, evaluating their correctness is equally problematic: how do we know if a reward function is correctly specified? In our work, we address these challenges by focusing on reward alignment -- assessing whether a reward function accurately encodes the preferences of a human stakeholder. As a concrete measure of reward alignment, we introduce the Trajectory Alignment Coefficient to quantify the similarity between a human stakeholder's ranking of trajectory distributions and those induced by a given reward function. We show that the Trajectory Alignment Coefficient exhibits desirable properties, such as not requiring access to a ground truth reward, invariance to potential-based reward shaping, and applicability to online RL. Additionally, in an 11 -- person user study of RL practitioners, we found that access to the Trajectory Alignment Coefficient during reward selection led to statistically significant improvements. Compared to relying only on reward functions, our metric reduced cognitive workload by 1.5x, was preferred by 82% of users and increased the success rate of selecting reward functions that produced performant policies by 41%.

Problem

• Addresses challenges in reward design for reinforcement learning.
• Introduces Trajectory Alignment Coefficient to measure reward alignment.
• Improves reward selection efficiency and success rate in RL.

Innovation

• Introduces Trajectory Alignment Coefficient for reward alignment.
• Quantifies similarity between human and reward-induced trajectory rankings.
• Reduces cognitive workload and improves reward function selection.