# Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning

arXiv: https://arxiv.org/abs/2606.30893 · HTML: https://mlsanity.com/paper/2606.30893/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 61 references that are in the map.
- First posted on arXiv 29 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 21 of 21)

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- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization](https://mlsanity.com/paper/2301.07784/) (2023)
- [Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview](https://mlsanity.com/paper/2110.00675/) (2021)
- [Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework](https://mlsanity.com/paper/2311.12495/) (2023)
- [MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning](https://mlsanity.com/paper/2603.00730/) (2026)
- [Nonlinear Opinion Dynamics with Tunable Sensitivity](https://mlsanity.com/paper/2009.04332/) (2020)
- [Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments](https://mlsanity.com/paper/2104.09705/) (2021)
- [MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2407.16312/) (2024)
- [Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2211.13032/) (2022)
- [Liquid-Graph Time-Constant Network for Multi-Agent Systems Control](https://mlsanity.com/paper/2404.13982/) (2024)
- [An Algorithm for Multi-Objective Multi-Agent Optimization](https://mlsanity.com/paper/2003.01745/) (2020)
- [Construction of Lyapunov functions for interconnected parabolic systems: an iISS approach](https://mlsanity.com/paper/1410.3058/) (2014)
- [Cluster synchronization of diffusively-coupled nonlinear systems: A contraction based approach](https://mlsanity.com/paper/1707.00748/) (2017)
- [MOMA-AC: A preference-driven actor-critic framework for continuous multi-objective multi-agent reinforcement learning](https://mlsanity.com/paper/2511.18181/) (2025)

## Abstract (arXiv)

Multi-robot systems must simultaneously optimize competing objectives while maintaining coordinated behavior. Existing multi-agent reinforcement learning approaches often rely on fixed or centralized coordination, which limits adaptability and violates distributed constraints. This work introduces the Coordination-Informed Multi-Objective Reinforcement Learning (CIMORL) framework, integrating a distributed weight prediction mechanism, a privileged expert training strategy, and theoretical guarantees for Pareto-optimal solutions. We present the base CIMORL method alongside two sampling-based variants, CIMORL-TS (Tree Search) and CIMORL-MPPI (MPPI), which leverage privileged global information during training to enable fully decentralized deployment. Experimental validation in cooperative and adversarial scenarios demonstrates a 21.2% hypervolume improvement and superior policy stability compared to state-of-the-art baselines. Real-world experiments with Crazyflie drones further validate the framework's robustness in resource allocation and multi-attacker multi-defend scenarios under partial observability.