# Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning

arXiv: https://arxiv.org/abs/2310.08746 · HTML: https://mlsanity.com/paper/2310.08746/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Action Robust Reinforcement Learning and Applications in Continuous Control](https://mlsanity.com/paper/1901.09184/) (2019)
- [Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning](https://mlsanity.com/paper/2210.05927/) (2022)
- [A Review of Uncertainty for Deep Reinforcement Learning](https://mlsanity.com/paper/2208.09052/) (2022)
- [Robust Multi-Agent Reinforcement Learning with State Uncertainty](https://mlsanity.com/paper/2307.16212/) (2023)
- [SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing](https://mlsanity.com/paper/2003.09347/) (2020)
- [A Curriculum Learning Method for Improved Noise Robustness in Automatic Speech Recognition](https://mlsanity.com/paper/1606.06864/) (2016)
- [What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?](https://mlsanity.com/paper/2212.02705/) (2022)
- [Robust Policy Learning over Multiple Uncertainty Sets](https://mlsanity.com/paper/2202.07013/) (2022)
- [ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation](https://mlsanity.com/paper/2109.06795/) (2021)
- [Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum](https://mlsanity.com/paper/2206.10057/) (2022)
- [RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments](https://mlsanity.com/paper/2209.05738/) (2022)
- [DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments](https://mlsanity.com/paper/2209.02865/) (2022)

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

- [RobustVLA: On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations](https://mlsanity.com/paper/2510.00037/) (2025)

## Abstract (arXiv)

Multi-agent reinforcement learning (MARL) plays a pivotal role in tackling real-world challenges. However, the seamless transition of trained policies from simulations to real-world requires it to be robust to various environmental uncertainties. Existing works focus on finding Nash Equilibrium or the optimal policy under uncertainty in one environment variable (i.e. action, state or reward). This is because a multi-agent system itself is highly complex and unstationary. However, in real-world situation uncertainty can occur in multiple environment variables simultaneously. This work is the first to formulate the generalised problem of robustness to multi-modal environment uncertainty in MARL. To this end, we propose a general robust training approach for multi-modal uncertainty based on curriculum learning techniques. We handle two distinct environmental uncertainty simultaneously and present extensive results across both cooperative and competitive MARL environments, demonstrating that our approach achieves state-of-the-art levels of robustness.