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

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.

• Publication year

  2025

• Venue

  arXiv.org

• Publication date

  2025-07-02

• Fields of study

  Computer Science

• Identifiers
  DOI 10.48550/arXiv.2507.01470arXiv 2507.01470
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Laser Learning Environment: A new environment for coordination-critical multi-agent tasks

  2024influential reference
• HiSOMA: A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning

  2024cited by this paper
• Research on Multi-agent Sparse Reward Problem

  2024cited by this paper
• Faster MIL-based Subgoal Identification for Reinforcement Learning by Tuning Fewer Hyperparameters

  2024cited by this paper
• An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains

  2023influential reference
• MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer

  2022influential reference
• Exploration in Deep Reinforcement Learning: A Survey

  2022cited by this paper
• HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism

  2021influential reference
• QPLEX: Duplex Dueling Multi-Agent Q-Learning

  2020cited by this paper
• The StarCraft Multi-Agent Challenge

  2019cited by this paper
• Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning

  2019cited by this paper
• Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards

  2019cited by this paper
• QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

  2018influential reference
• Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward

  2018cited by this paper
• Hindsight Experience Replay

  2017cited by this paper
• Curiosity-Driven Exploration by Self-Supervised Prediction

  2017cited by this paper
• Concrete Problems in AI Safety

  2016cited by this paper
• #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

  2016cited by this paper
• Deep Reinforcement Learning with Double Q-Learning

  2015cited by this paper
• Automatic Discovery of Subgoals in Reinforcement Learning Using Strongly Connected Components

  2008cited by this paper
• Identifying useful subgoals in reinforcement learning by local graph partitioning

  2005cited by this paper
• Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density

  2001cited by this paper
• Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning

  1999influential reference
• Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping

  1999cited by this paper
• Catastrophic forgetting in connectionist networks.

  1999influential reference
• Gradient-based learning applied to document recognition

  1998cited by this paper
• Learning to Drive a Bicycle Using Reinforcement Learning and Shaping

  1998cited by this paper
• Reinforcement Learning: An Introduction

  1998cited by this paper
• Normalized cuts and image segmentation

  1997cited by this paper
• Dynamic Programming

  1993influential reference
• Introduction to graph theory

  1973influential reference

• No citing papers are available for this paper.