Multi-Agent Reinforcement Learning (MARL) has been widely applied in many fields such as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are vulnerable to adversarial perturbations on agent states. Robustness testing for a trained model is an essential step for confirming the trustworthiness of the model against unexpected perturbations. This work proposes a novel Robustness Testing framework for MARL that attacks states of Critical Agents (RTCA). The RTCA has two innovations: 1) a Differential Evolution (DE) based method to select critical agents as victims and to advise the worst-case joint actions on them; and 2) a team cooperation policy evaluation method employed as the objective function for the optimization of DE. Then, adversarial state perturbations of the critical agents are generated based on the worst-case joint actions. This is the first robustness testing framework with varying victim agents. RTCA demonstrates outstanding performance in terms of the number of victim agents and destroying cooperation policies.

https://export.arxiv.org/pdf/2306.06136v1.pdf

Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents

Multi-agent reinforcement learning (MARL) has achieved promising results in recent years. However, most existing reinforcement learning methods require a large amount of data for model training. In addition, data-efficient reinforcement learning requires the construction of strong inductive biases, which are ignored in the current MARL approaches. Inspired by the symmetry phenomenon in multi-agent systems, this paper proposes a framework for exploiting prior knowledge by integrating data augmentation and a well-designed consistency loss into the existing MARL methods. In addition, the proposed framework is model-agnostic and can be applied to most of the current MARL algorithms. Experimental tests on multiple challenging tasks demonstrate the effectiveness of the proposed framework. Moreover, the proposed framework is applied to a physical multi-robot testbed to show its superiority.

pdf/esp-exploiting-symmetry-prior-for-multi-agent-reinforcement-379gcv2jkd.pdf

ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning

Cooperative multiagent reinforcement learning approaches are increasingly being used to make decisions in contested and dynamic environments, which tend to be wildly different from the environments used to train them. As such, there is a need for a more in-depth understanding of their resilience and robustness in conditions such as network partitions, node failures, or attacks. In this article, we propose a modeling and simulation framework that explores the resilience of four c-MARL models when faced with different types of attacks, and the impact that training with different perturbations has on the effectiveness of these attacks. We show that c-MARL approaches are highly vulnerable to perturbations of observation, action reward, and communication, showing more than 80% drop in the performance from the baseline. We also show that appropriate training with perturbations can dramatically improve performance in some cases, however, can also result in overfitting, making the models less resilient against other attacks. This is a first step toward a more in-depth understanding of the resilience c-MARL models and the effect that contested environments can have on their behavior and toward resilience of complex systems in general.

A simulation and experimentation architecture for resilient cooperative multiagent reinforcement learning models operating in contested and dynamic environments

In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in training versus reliance on local observations in execution, lacking global signals. Inspired by human societal consensus mechanisms, we introduce the Hierarchical Consensus-based Multi-Agent Reinforcement Learning (HC-MARL) framework to address this limitation. HC-MARL employs contrastive learning to foster a global consensus among agents, enabling cooperative behavior without direct communication. This approach enables agents to form a global consensus from local observations, using it as an additional piece of information to guide collaborative actions during execution. To cater to the dynamic requirements of various tasks, consensus is divided into multiple layers, encompassing both short-term and long-term considerations. Short-term observations prompt the creation of an immediate, low-layer consensus, while long-term observations contribute to the formation of a strategic, high-layer consensus. This process is further refined through an adaptive attention mechanism that dynamically adjusts the influence of each consensus layer. This mechanism optimizes the balance between immediate reactions and strategic planning, tailoring it to the specific demands of the task at hand. Extensive experiments and real-world applications in multi-robot systems showcase our framework's superior performance, marking significant advancements over baselines. 

pdf/hierarchical-consensus-based-multi-agent-reinforcement-3ob7yqusvj.pdf

Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for
  Multi-Robot Cooperation Tasks

Recently, ChatGPT or InstructGPT like large language models (LLM) has made a significant impact in the AI world. These models are incredibly versatile, capable of performing language tasks on par or even exceeding the capabilities of human experts. Many works have attempted to reproduce the complex InstructGPT's RLHF (Reinforcement Learning with Human Feedback) training pipeline. However, the mainstream distributed RLHF training methods typically adopt a fixed model placement strategy, referred to as the Flattening strategy. This strategy treats all four models involved in RLHF as a single entity and places them on all devices, regardless of their differences. Unfortunately, this strategy exacerbates the generation bottlenecks in the RLHF training and degrades the overall training efficiency. To address these issues, we propose an adaptive model placement framework that offers two flexible model placement strategies. These strategies allow for the agile allocation of models across devices in a fine-grained manner. The Interleaving strategy helps reduce memory redundancy and communication costs during RLHF training. On the other hand, the Separation strategy improves the throughput of model training by separating the training and generation stages of the RLHF pipeline. Notably, this framework seamlessly integrates with other mainstream techniques for acceleration and enables automatic hyperparameter search. Extensive experiments have demonstrated that our Interleaving and Separation strategies can achieve notable improvements up to 11x, compared to the current state-of-the-art (SOTA) approaches. These experiments encompassed a wide range of training scenarios, involving models of varying sizes and devices of different scales. The results highlight the effectiveness and superiority of our approaches in accelerating the training of distributed RLHF.

An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training

Autonomous collision avoidance is a critical component in various multi-robot applications. While deep reinforcement learning (RL) has demonstrated success in some robotic control tasks, it remains challenging to apply it to real-world multi-agent collision tasks due to poor sample efficiency. The limited amount of transition data available and its strong correlation with multi-agent task characteristics have restricted recent research in this area. In this paper, we propose Multi-agent Collision Avoidance with Continuous Transition Reinforcement Learning via Mixup (MACT) to address these challenges. MACT generates new continuous transitions for training by linearly interpolating consecutive transitions. To ensure the authenticity of constructed transitions, we develop a discriminator that automatically adjusts the mixup parameters. Our proposed approach is evaluated through simulations and real-world swarm robots consisting of E-pucks, demonstrating its practical application. Our learned policies outperform existing collision avoidance methods in terms of safety and efficiency. 

MACT: Multi-agent Collision Avoidance with Continuous Transition Reinforcement Learning via Mixup

Symmetry-Guided Multi-Agent Inverse Reinforcement Learning

This study probes the vulnerabilities of cooperative multi-agent reinforcement learning (c-MARL) under adversarial attacks, a critical determinant of c-MARL's worst-case performance prior to real-world implementation. Current observation-based attacks, constrained by white-box assumptions, overlook c-MARL's complex multi-agent interactions and cooperative objectives, resulting in impractical and limited attack capabilities. To address these shortcomes, we propose Adversarial Minority Influence (AMI), a practical and strong for c-MARL. AMI is a practical black-box attack and can be launched without knowing victim parameters. AMI is also strong by considering the complex multi-agent interaction and the cooperative goal of agents, enabling a single adversarial agent to unilaterally misleads majority victims to form targeted worst-case cooperation. This mirrors minority influence phenomena in social psychology. To achieve maximum deviation in victim policies under complex agent-wise interactions, our unilateral attack aims to characterize and maximize the impact of the adversary on the victims. This is achieved by adapting a unilateral agent-wise relation metric derived from mutual information, thereby mitigating the adverse effects of victim influence on the adversary. To lead the victims into a jointly detrimental scenario, our targeted attack deceives victims into a long-term, cooperatively harmful situation by guiding each victim towards a specific target, determined through a trial-and-error process executed by a reinforcement learning agent. Through AMI, we achieve the first successful attack against real-world robot swarms and effectively fool agents in simulated environments into collectively worst-case scenarios, including Starcraft II and Multi-agent Mujoco. The source code and demonstrations can be found at: https://github.com/DIG-Beihang/AMI.

Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence

Incorporating symmetry as an inductive bias into multi-agent reinforcement learning (MARL) has led to improvements in generalization, data efficiency, and physical consistency. While prior research has succeeded in using perfect symmetry prior, the realm of partial symmetry in the multi-agent domain remains unexplored. To fill in this gap, we introduce the partially symmetric Markov game, a new subclass of the Markov game. We then theoretically show that the performance error introduced by utilizing symmetry in MARL is bounded, implying that the symmetry prior can still be useful in MARL even in partial symmetry situations. Motivated by this insight, we propose the Partial Symmetry Exploitation (PSE) framework that is able to adaptively incorporate symmetry prior in MARL under different symmetry-breaking conditions. Specifically, by adaptively adjusting the exploitation of symmetry, our framework is able to achieve superior sample efficiency and overall performance of MARL algorithms. Extensive experiments are conducted to demonstrate the superior performance of the proposed framework over baselines. Finally, we implement the proposed framework in real-world multi-robot testbed to show its superiority.

pdf/leveraging-partial-symmetry-for-multi-agent-reinforcement-3rwap13xty.pdf

Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning

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