# Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains

arXiv: https://arxiv.org/abs/2502.19297 · HTML: https://mlsanity.com/paper/2502.19297/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.07169/) (2020)
- [From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey](https://mlsanity.com/paper/2108.11451/) (2021)
- [Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery](https://mlsanity.com/paper/1912.03558/) (2019)
- [ALMA: Hierarchical Learning for Composite Multi-Agent Tasks](https://mlsanity.com/paper/2205.14205/) (2022)
- [Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model](https://mlsanity.com/paper/2208.10458/) (2022)
- [Fitted Q-Learning for Relational Domains](https://mlsanity.com/paper/2006.05595/) (2020)

## Abstract (arXiv)

Multiagent Reinforcement Learning (MARL) poses significant challenges due to the exponential growth of state and action spaces and the non-stationary nature of multiagent environments. This results in notable sample inefficiency and hinders generalization across diverse tasks. The complexity is further pronounced in relational settings, where domain knowledge is crucial but often underutilized by existing MARL algorithms. To overcome these hurdles, we propose integrating relational planners as centralized controllers with efficient state abstractions and reinforcement learning. This approach proves to be sample-efficient and facilitates effective task transfer and generalization.