TL;DR
This paper introduces a framework for learning incentive structures in multi-agent systems to enhance cooperative resilience under social dilemmas, demonstrating that hybrid incentives improve system stability and performance during disruptions.
Contribution
The work presents a novel method for inferring and integrating incentive structures that promote resilient collective behavior in multi-agent reinforcement learning systems facing social dilemmas.
Findings
Hybrid incentive structures reduce collapse events and resource depletion.
Resilience-aligned incentives improve sustained collective behavior.
The framework effectively scores and ranks agent trajectories to inform incentive design.
Abstract
Multi-agent social dilemmas, such as the tragedy of the commons, capture settings where individual incentives conflict with collective well-being, making these systems highly vulnerable to collapse under disruptions. In this context, this work studies cooperative resilience, understood as the system-level ability to maintain collective well-being under perturbations through adaptive agent behavior. We propose a framework for learning incentive structures aligned with collective well-being in multi-agent reinforcement learning systems, where reward functions shape individual decision-making and collective behavior. A resilience metric is used to score and rank agent trajectories, allowing the inference of reward functions that promote resilient collective behavior. These inferred reward functions are integrated into the multi-agent reinforcement learning process to shape agent…
Peer Reviews
Decision·Submitted to ICLR 2026
- Originality The primary original contribution is the robust methodology for grounding reward inference in a quantitative, system-level metric of cooperative resilience. While Inverse Reinforcement Learning (IRL) is not new, using preference-based IRL (MPL/PPL) derived from trajectories ranked explicitly by their recovery and failure profiles under stress is a novel application pathway for incentive design in MARL. This approach circumvents the conventional IRL dependence on near-optimal exper
- Quality The experimental quality suffers from weaknesses in the baseline selection and the dependence on parameterization. The paper compares performance against basic PPO and QMIX and explicitly notes the omission of more recent, high-performing cooperative MARL algorithms. This leaves a significant open question regarding the necessity of the complex two-stage IRL process compared to simpler, modern reward shaping or decentralized planning techniques. Furthermore, the QMIX baseline required
- Clear and coherent methodology. The proposed pipeline of ranking trajectories, learning preferences, and inferring rewards is logically structured and mathematically sound. - Novel IRL formulation for resilience. - Reproducibility. The paper provides detailed appendices, configurations, and discusses reproducibility assets and ethical considerations, which is commendable. - Practical potential. The idea of learning system-level incentives from ranked behaviors could, in principle, be applied i
- Metric-evaluation circularity. The same cooperative-resilience metric used for ranking trajectories is also used to evaluate success. This makes it impossible to tell whether agents actually learned to be resilient or simply optimized the evaluator. The fact that disruptions occur at the same fixed timestep in both training and testing further amplifies this problem. - Uninformative supervision data. Rankings are generated from random-policy trajectories, which are likely dominated by stochas
The paper’s main strengths are its clear and timely problem framing—learning reward functions that encode cooperative resilience rather than handcrafting sustainability terms—coupled with a well-structured pipeline that converts trajectory-level resilience scores into pairwise preferences and trains reward models (margin-based or probabilistic) that plug seamlessly into standard MARL; it explores multiple reward parameterizations (handcrafted, linear, neural) and a practical hybrid objective (in
1. The resilience metric is manually constructed as a harmonic mean over several indicators with fixed weights and failure/recovery windows. The paper does not study how these design choices affect trajectory rankings or learned rewards. Because the metric defines the training signal, its sensitivity is a critical missing analysis. 2. Rewards are learned from single-shock episodes (one disruption at step 500) but tested on triple-shock long runs. The authors claim generalization to unseen disru
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Taxonomy
TopicsReinforcement Learning in Robotics · Evolutionary Game Theory and Cooperation · Infrastructure Resilience and Vulnerability Analysis
