Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals

TL;DR

This paper investigates the limitations of reinforcement learning in scenarios where crucial subgoals do not provide immediate rewards, revealing challenges in current algorithms and emphasizing the need for methods that infer task structure without relying solely on incentives.

Contribution

The work formalizes zero-incentive dynamics, highlighting a structural challenge in RL where essential subgoals lack rewards, and demonstrates the failure of current deep subgoal algorithms in such settings.

Findings

Current algorithms struggle with unrewarded critical subgoals.

Performance depends heavily on the timing of rewards relative to subgoal completion.

A fundamental limitation exists in leveraging unrewarded subgoal dynamics.

Abstract

This work re-examines the commonly held assumption that the frequency of rewards is a reliable measure of task difficulty in reinforcement learning. We identify and formalize a structural challenge that undermines the effectiveness of current policy learning methods: when essential subgoals do not directly yield rewards. We characterize such settings as exhibiting zero-incentive dynamics, where transitions critical to success remain unrewarded. We show that state-of-the-art deep subgoal-based algorithms fail to leverage these dynamics and that learning performance is highly sensitive to the temporal proximity between subgoal completion and eventual reward. These findings reveal a fundamental limitation in current approaches and point to the need for mechanisms that can infer latent task structure without relying on immediate incentives.