# TIGER-MARL: Enhancing Multi-Agent Reinforcement Learning with Temporal Information through Graph-based Embeddings and Representations

arXiv: https://arxiv.org/abs/2511.08832 · HTML: https://mlsanity.com/paper/2511.08832/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2025.

## Built on by (most cited first, 3 of 3)

- [Action-Graph Policies: Learning Action Co-dependencies in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.17009/) (2026)
- [Do LLM-derived graph priors improve multi-agent coordination?](https://mlsanity.com/paper/2604.17191/) (2026)
- [SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.08391/) (2026)

## Abstract (arXiv)

In this paper, we propose capturing and utilizing textitTemporal Information through Graph-based Embeddings and Representations or TIGER to enhance multi-agent reinforcement learning (MARL). We explicitly model how inter-agent coordination structures evolve over time. While most MARL approaches rely on static or per-step relational graphs, they overlook the temporal evolution of interactions that naturally arise as agents adapt, move, or reorganize cooperation strategies. Capturing such evolving dependencies is key to achieving robust and adaptive coordination. To this end, TIGER constructs dynamic temporal graphs of MARL agents, connecting their current and historical interactions. It then employs a temporal attention-based encoder to aggregate information across these structural and temporal neighborhoods, yielding time-aware agent embeddings that guide cooperative policy learning. Through extensive experiments on two coordination-intensive benchmarks, we show that TIGER consistently outperforms diverse value-decomposition and graph-based MARL baselines in task performance and sample efficiency. Furthermore, we conduct comprehensive ablation studies to isolate the impact of key design parameters in TIGER, revealing how structural and temporal factors can jointly shape effective policy learning in MARL. All codes can be found here: https://github.com/Nikunj-Gupta/tiger-marl.