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1. What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?

   Han, Songyang; Su, Sanbao; He, Sihong; Han, Shuo; Yang, Haizhao; Zou, Shaofeng; Miao, Fei (February 2024, Transactions on Machine Learning Research)

   Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents’ policies are based on accurate state information. However, policies learned through Deep Reinforcement Learning (DRL) are susceptible to adversarial state perturbation attacks. In this work, we propose a State-Adversarial Markov Game (SAMG) and make the first attempt to investigate different solution concepts of MARL under state uncertainties. Our analysis shows that the commonly used solution concepts of optimal agent policy and robust Nash equilibrium do not always exist in SAMGs. To circumvent this difficulty, we consider a new solution concept called robust agent policy, where agents aim to maximize the worst-case expected state value. We prove the existence of robust agent policy for finite state and finite action SAMGs. Additionally, we propose a Robust Multi-Agent Adversarial Actor-Critic (RMA3C) algorithm to learn robust policies for MARL agents under state uncertainties. Our experiments demonstrate that our algorithm outperforms existing methods when faced with state perturbations and greatly improves the robustness of MARL policies. Our code is public on https://songyanghan.github.io/what_is_solution/. 

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2. Asynchronous multi-agent deep reinforcement learning under partial observability

   https://doi.org/10.1177/02783649241306124  

   Xiao, Yuchen; Tan, Weihao; Hoffman, Joshua; Xia, Tian; Amato, Christopher (July 2025, The International Journal of Robotics Research)

   The state-of-the-art multi-agent reinforcement learning (MARL) methods provide promising solutions to a variety of complex problems. Yet, these methods all assume that agents perform primitive actions in a synchronized manner, making them impractical for long-horizon real-world multi-robot tasks that inherently require robots to asynchronously reason about action selection at varying time durations. To solve this problem, we first propose a group of value-based cooperative MARL approaches for asynchronous execution using temporally extended macro-actions. Here, agents perform asynchronous learning and decision-making with macro-action-value functions in three paradigms: decentralized learning and control, centralized learning and control, and centralized training for decentralized execution (CTDE). Building on the above work, we formulate a set of macro-action-based policy gradient algorithms under the three training paradigms, where agents directly optimize their parameterized policies in an asynchronous manner. We evaluate our methods both in simulation and on real robots over a variety of realistic domains. Empirical results demonstrate the effectiveness of our algorithms for learning high-quality and asynchronous solutions with macro-actions in large multi-agent problems that were previously unsolvable via primitive-action-based approaches. The proposed approaches represent the first general MARL methods for temporally extended actions and serve as the foundation for future methods in the area. 

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3. Explaining Decentralized Multi-Agent Reinforcement Learning Policies

   https://doi.org/10.1609/aaai.v40i21.38788  

   Boggess, Kayla; Kraus, Sarit; Feng, Lu (March 2026, Proceedings of the AAAI Conference on Artificial Intelligence)

   Multi-Agent Reinforcement Learning (MARL) has gained significant interest in recent years, enabling sequential decision-making across multiple agents in various domains. However, most existing explanation methods focus on centralized MARL, failing to address the uncertainty and nondeterminism inherent in decentralized settings. We propose methods to generate policy summarizations that capture task ordering and agent cooperation in decentralized MARL policies, along with query-based explanations for “When,” “Why Not,” and “What” types of user queries about specific agent behaviors. We evaluate our approach across four MARL domains and two decentralized MARL algorithms, demonstrating its generalizability and computational efficiency. User studies show that our summarizations and explanations significantly improve user question-answering performance and enhance subjective ratings on metrics such as understanding and satisfaction. 

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4. An Imitation Learning Method with Multi-Virtual Agents for Microgrid Energy Optimization under Interrupted Periods

   https://doi.org/10.1109/PESGM51994.2024.10689197  

   Lin, Yanbin; Ni, Zhen; Tang, Yufei (July 2024, IEEE)

   Existing computer analytic methods for the microgrid system, such as reinforcement learning (RL) methods, suffer from a long-term problem with the empirical assumption of the reward function. To alleviate this limitation, we propose a multi-virtual-agent imitation learning (MAIL) approach to learn the dispatch policy under different power supply interrupted periods. Specifically, we utilize the idea of generative adversarial imitation learning method to do direct policy mapping, instead of learning from manually designed reward functions. Multi-virtual agents are used for exploring the relationship of uncertainties and corresponding actions in different microgrid environments in parallel. With the help of a deep neural network, the proposed MAIL approach can enhance robust ability by minimizing the maximum crossover discriminators to cover more interrupted cases. Case studies show that the proposed MAIL approach can learn the dispatch policies as well as the expert method and outperform other existing RL methods. 

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5. Sample-efficient robust multi-agent reinforcement learning in the face of environmental uncertainty

   Shi, Laixi; Mazumdar, Eric; Chi, Yuejie; Wierman, Adam (July 2024, Proceedings of the 41st International Conference on Machine Learning)

   To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely studied in single-agent regimes, in multi-agent environments, the problem remains understudied-- despite the fact that the problems posed by environmental uncertainties are often exacerbated by strategic interactions. This work focuses on learning in distributionally robust Markov games (RMGs), a robust variant of standard Markov games, wherein each agent aims to learn a policy that maximizes its own worst-case performance when the deployed environment deviates within its own prescribed uncertainty set. This results in a set of robust equilibrium strategies for all agents that align with classic notions of game-theoretic equilibria. Assuming a non-adaptive sampling mechanism from a generative model, we propose a sample-efficient model-based algorithm (DRNVI) with finite-sample complexity guarantees for learning robust variants of various notions of game-theoretic equilibria. We also establish an information-theoretic lower bound for solving RMGs, which confirms the near-optimal sample complexity of DR-NVI with respect to problem-dependent factors such as the size of the state space, the target accuracy, and the horizon length. 

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