# Optimization without Future Compromises? Decentralized Coordination via Collective and Reinforcement Learning

arXiv: https://arxiv.org/abs/2509.18088 · HTML: https://mlsanity.com/paper/2509.18088/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multi-agent reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [Multi-agent reinforcement learning](https://mlsanity.com/deeprl/multi-agent-reinforcement-learning/)

## Builds on (most cited first, 20 of 20)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [Deep Reinforcement Learning for Multi-objective Optimization](https://mlsanity.com/paper/1906.02386/) (2019)
- [Exploratory Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1909.04063/) (2019)
- [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization](https://mlsanity.com/paper/2006.01610/) (2020)
- [Temporal Abstraction in Reinforcement Learning with the Successor Representation](https://mlsanity.com/paper/2110.05740/) (2021)
- [Deep Multiagent Reinforcement Learning: Challenges and Directions](https://mlsanity.com/paper/2106.15691/) (2021)
- [Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation](https://mlsanity.com/paper/2211.02127/) (2022)
- [HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism](https://mlsanity.com/paper/2110.07246/) (2021)
- [ALMA: Hierarchical Learning for Composite Multi-Agent Tasks](https://mlsanity.com/paper/2205.14205/) (2022)
- [PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.08872/) (2022)
- [Exact Reduction of Huge Action Spaces in General Reinforcement Learning](https://mlsanity.com/paper/2012.10200/) (2020)
- [Self-regulating Supply-Demand Systems](https://mlsanity.com/paper/1701.04679/) (2017)
- [Discrete-choice Multi-agent Optimization: Decentralized Hard Constraint Satisfaction for Smart Cities](https://mlsanity.com/paper/2305.16993/) (2023)
- [Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation](https://mlsanity.com/paper/2302.01728/) (2023)
- [Strategic Coordination of Drones via Short-term Distributed Optimization and Long-term Reinforcement Learning](https://mlsanity.com/paper/2311.09852/) (2023)

## Built on by (most cited first, 1 of 1)

- [Rollout-Training Co-Design for Efficient LLM-Based Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.09578/) (2026)

## Abstract (arXiv)

Efficient resource allocation in multi-agent systems requires autonomous agents to coordinate their decisions while balancing system-wide objectives with individual costs. This becomes increasingly challenging over long time horizons, where decisions that improve the current allocation may compromise future resource allocation, while decentralized agents have limited observations of the overall system. Multi-agent reinforcement learning (MARL) can learn such long-term dependencies via local observations, but directly applying it to large-scale coordination leads to rapidly growing decision spaces and inefficient training. To this end, we propose Hierarchical Reinforcement and Collective Learning (HRCL), a hierarchical framework that uses MARL to guide, rather than replace, decentralized multi-agent coordination. At the high level, MARL learns strategies that restrict the alternatives considered during coordination and guide agents in balancing system-wide and individual objectives. At the low level, agents perform efficient decentralized coordination under this strategic guidance. This separation reduces the learning space and allows short-term coordination trade-offs to be evaluated according to their long-term effects. Experiments on a synthetic benchmark show that HRCL converges substantially faster than standalone MARL and reduces system-wide and individual costs by 35.53% and 27.05%, respectively. Evaluations on energy self-management and drone swarm sensing further show improved resource allocation, power-peak regulation, and sensing efficiency. These results show that learning strategic guidance for an existing coordination process can retain scalable decentralized coordination without letting short-term decisions compromise future resource allocation.