Multi agent reinforcement learning pdf. Albrecht, Filippos Christianos, Lukas Schäfer.
Multi agent reinforcement learning pdf cn wangyw@pcl. The MARL Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Ting Zhu1, Yue Jin2, Jeremie Houssineau3, Giovanni Montana1,2,4 1Department of Statistics, University of Warwick, Coventry, UK, 2Warwick Manufacturing Group, University of Warwick, Coventry, UK, 3School of Physical & Mathematical Sciences, Nanyang Technological View PDF HTML (experimental) Abstract: Offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn optimal multi-agent policies from pre-collected datasets. Ahmed and Cillian Brewitt and Ignacio Carlucho and Filippos Christianos and Mhairi Dunion and Elliot Fosong and Samuel Garcin and Shangmin Multi-agent reinforcement learning (MARL) is concerned with a set of autonomous agents that share a common en-vironment (Busoniu et al. Tasks involving distributed multi-robots [3], [4] often require several agents to collaborate based on their local observations to accomplish a given objective. Multi-agent systems can be used to address problems in a variety of domains, including robotics, distributed control, telecommunications, and economics. Albrecht, Filippos Christianos, Lukas Schäfer. In this paper we demonstrate the use of Vogue, a high performance agent based model (ABM) framework. In this context, the present work deals with the question of how to control the diversity of a multi-agent View PDF Abstract: Even though Google Research Football (GRF) was initially benchmarked and studied as a single-agent environment in its original paper, recent years have witnessed an increasing focus on its multi-agent nature by researchers utilizing it as a testbed for Multi-Agent Reinforcement Learning (MARL). Multi-agent reinforcement learning for robotic problems Multi-agent reinforcement learning extends reinforcement learning approaches to problems where multiple agents inter-act in the environment [45]–[50]. In multi-agent environments, agents must interact with each other, where the interaction relationship includes View PDF HTML (experimental) Abstract: The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research. In this MARL approach, each agent considers all the other agents to be part of the envi- Figure 1: Illustration of conventional reinforcement learning. busoniu@tudelft. 01769 Corpus ID: 251280270; Deep Reinforcement Learning for Multi-Agent Interaction @article{Ahmed2022DeepRL, title={Deep Reinforcement Learning for Multi-Agent Interaction}, author={Ibrahim H. However, current studies and applications need to address its scalability, non-stationarity, and Reinforcement learning (RL) has been an active research area in AI for many years. wang@kcl. Vogue serves as a multi-agent training environment, View PDF Abstract: Many scenarios in mobility and traffic involve multiple different agents that need to cooperate to find a joint solution. com, {liwenhao, zhahy, bxiangwang}@cuhk. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating PDF | On Jan 1, 2025, Rajesh Kumar Malviya and others published Reinforcement Learning for Collaborative Decision-Making in Multi-Agent Systems: Applications in Supply Chain Optimization | Find Multi-agent reinforcement learning has a rich literature [8, 30]. edu {suntao, yunzhet, sahika, smallya}@amazon. The body of Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the agents in a fully decentralized manner. lu@pku. Learning in MARL is fundamentally difficult since agents not only interact with the environment but also with each other. However, despite the impressive achievements, it is still necessary The advances in reinforcement learning have recorded sublime success in various domains. Inverse Reinforcement Learning (IRL) is a well-established approach to inferring the utility function by observing an expert behavior within a PDF | Reinforcement learning in a multi-agent setting is very important for real-world applications, but it brings more challenges than those in a | Find, read and cite all the research you Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches Sanyam Kapoor sanyam@nyu. This impedes agents from performing the policy improvement step of Q PDF | Recent Multi-Agent Reinforcement Learning (MARL) literature has been largely focused on Centralized Training with Decentralized Execution (CTDE) | Find, read and cite all the research you Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. , 2021] . RHMC divides the coordination task into two Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Kuroswiski1,2, Annie S. However, many research endeavours heavily rely on parameter sharing among agents, which confines them to only homogeneous-agent setting and leads to training instability and Request PDF | Multi-Agent Reinforcement Learning is a Sequence Modeling Problem | Large sequence model (SM) such as GPT series and BERT has displayed outstanding performance and generalization Cooperation and Fairness in Multi-Agent Reinforcement Learning 3 •We show that our method scales to allow an arbitrary number of agents to create any formation shape without the need for retraining. Independent Q-learning (Tan, 1993) that considers other agents as a part of the environment often fails as the The advances in reinforcement learning have recorded sublime success in various domains. ac. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning (“MARL”), by making work more interchangeable, accessible and re-producible akin to what OpenAI’s Gym library did for single-agent reinforcement learning. View PDF HTML (experimental) Abstract: Deep cooperative multi-agent reinforcement learning has demonstrated its remarkable success over a wide spectrum of complex control tasks. We pinpoint this We apply Multi-Agent Deep Reinforcement Learning (MADRL) to inventory management problems with multiple echelons and evaluate MADRL’s performance to minimize the overall costs of a supply chain. Unlike traditional reinforcement learning (RL) that is applicable to single-agent This paper shows that a additional sensation from another agent is bene cial if it can be used to speeds up learning at the cost of communica tion and for joint tasks agents engaging in partnership can outperform independent agents We first online how deep reinforcement learning works in single agent systems. , 2008). This work introduces a novel approach for solving reinforcement learning Keywords Multi-Agent Reinforcement Learning ·Deep Reinforcement Learning ·Communication ·Survey 1 Introduction Many real-world scenarios, such as autonomous driving [1], sensor networks [2], robotics [3] and game-playing [4, 5], can be modeled as multi-agent systems. Compared to single-agent case, multi-agent setting involves a large joint state-action space and coupled behaviors of multiple agents, which bring extra complexity to offline This work proposes a state reformulation of multi-agent problems in R that allows the system state to be represented in an image-like fashion and applies deep reinforcement learning techniques with a convolution neural network as the Q-value function approximator to learn distributed multi- agent policies. Published by MIT Press, 2024 The first comprehensive introduction to multi-agent reinforcement learning, an area of machine learning in which multiple decision-making agents learn to optimally interact in a shared environment. Multi-Agent Reinforcement Learning (MARL) still faces significant challenges that must be over-come to unlock its full potential; one such challenge is the ability to scale to large numbers of agents while maintaining good performance. However, it suffers from certain drawbacks like deceptive reward Multi-agent reinforcement learning, an essential branch in artificial intelligence, has demonstrated broad application prospects across various domains, including Go [] and StarCraft II [] in recent years. 3. Deep Reinforcement Learning In reinforcement learning, an agent interacting with its environment is attempting to learn an optimal control pol-icy. MARL corresponds to the learning problem in a multi-agent system in which multiple agents learn simultaneously. INTRODUCTION A multi-agent system [1] can be defined as a group of au-tonomous, In this chapter, we provide a selective overview of MARL, with focus on algorithms backed by theoretical analysis. babuska@tudelft. Compared to single-agent case, multi-agent setting involves a large joint state-action space and coupled behaviors of multiple agents, which bring extra complexity to offline policy optimization. Due to the lack of information about other agents, it is challenging to derive algorithms that can converge to the optimal joint policy in a fully decentralized setting. Inspired by how human beings learn from trial and error, MARL Index Terms—Multi-agent Reinforcement Learning, Multi-agent Systems, Agent Modeling. Advancing MARL methods has various potential real-world applications, as a lot of these problems can be naturally described as multi-agent systems: robotics, self-driving cars, View PDF HTML (experimental) Abstract: In multi-agent systems, the agent behavior is highly influenced by its utility function, as these utilities shape both individual goals as well as interactions with the other agents. Recent advances in behavioral planning use Reinforcement Learning to find effective and performant behavior strategies. 2. The cooperative knowledge and policies learned in non-hierarchical algorithms are implicit and not Multi-Agent Reinforcement Learning (MARL) is garner-ing increasing attention for its capability to tackle complex tasks [1], [2]. Lee§ Abstract Policy optimization methods with function approximation are widely used in multi-agent reinforcement learning. While deep RL has Multi-Agent Reinforcement Learning (MARL) research is advancing significantly based on the issues of poor scalability and non-stationary and has shown remarkable success in a range of applications. Our classification of MARL approaches includes five categories for modeling and solving cooperative multi-agent Learning to Share in Multi-Agent Reinforcement Learning Yuxuan Yi 1Ge Li Yaowei Wang2 Zongqing Lu y 1Peking University 2Peng Cheng Lab {touma,geli}@pku. In a multi-agent system (MAS), each agent interacts within the environment and is capable of taking actions based on environmental cues and opponents’ reactions. We then propose our approach to extending deep reinforcement learning to multi-agent sys-tems. In this paper, we adopt general-sum stochastic games as This chapter reviews a representative selection of multi-agent reinforcement learning algorithms for fully cooperative, fully competitive, and more general (neither cooperative nor competitive) tasks. However, current studies and applications need to address its scalability, non-stationarity, and trustworthiness. At the same time, it is often possible to train the agents in a DOI: 10. In this review article, we have mostly focused | Find, read and cite all the research you Multi-Agent Deep Reinforcement Learning with Human Strategies Thanh Nguyen, Ngoc Duy Nguyen, and Saeid Nahavandi Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University, Waurn Ponds Campus Geelong, VIC, 3216, Australia E-mails:{thanh. This article provides an In this section, we provide the necessary background on reinforcement learning, in both single- and multi-agent settings. This approach to learning has received immense interest in recent times and success Safe Multi-agent Reinforcement Learning with Natural Language Constraints Ziyan Wang1, Meng Fang2, Tristan Tomilin3, Fei Fang4, Yali Du1 1 King’s College London 2 University of Liverpool 3 Eindhoven University of Technology 4 Carnegie Mellon University ziyan. nl, feifang@cmu. We cover numerous MADRL perspectives, including non A multiagent Q-learning method is designed under general-sum stochastic games, and it is proved that it converges to a Nash equilibrium under speci ed conditions. brown. A number of algorithms involve value function based cooperative learning. I. In such systems, the optimal policy of an agent depends not only on the Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when A multi-agent reinforcement learning (MARL) frame- work is fundamentally no different from single agent RL. ,2022) boasts state-of-the-art performance in online MARL. However, a significant drawback of Transformer models is their quadratic Multi-Agent Reinforcement Learning (MARL) has been successful in solving many cooperative challenges. [29] iden-tified modularity as a useful prior to simplify the application of reinforcement learning methods to multiple agents. However, classic non-hierarchical MARL algorithms still cannot address various complex multi-agent problems that require hierarchical cooperative behaviors. edu, yali. uk Information Design in Multi-Agent Reinforcement Learning Yue Lin, Wenhao Li, Hongyuan Zha, Baoxiang Wang∗ The Chinese University of Hong Kong, Shenzhen linyue3h1@gmail. A multi-agent system is a group of autonomous, interacting entities sharing a com-mon environment, which they perceive with sensors and upon which they act with actuators This chapter reviews a representative selection of multi-agent reinforcement learning algorithms for fully cooperative, fully competitive, and more general (neither cooperative nor The first comprehensive introduction to multi-agent reinforcement learning, an area of machine learning in which multiple decision-making agents learn to optimally interact in a shared environment. Stefano V. We delve into the Multi-agent Reinforcement Learning (MARL) [8] involves more than one agent situated in the same environment compet-ing against each other, cooperating to achieve a common goal or a mix of the two. nguyen,saeid. We summarize the relevant research on MARL in nine domains, involved in engineering and science. In fact, a simple approach is to independently train all agents. The setting is Multi-Agent Reinforcement Learning: An Overview Lucian Bus¸oniu1, Robert Babuskaˇ 2, and Bart De Schutter3 1 Center for Systems and Control, Delft University of Technology, The Netherlands, i. ,2020;Li et al. The Multi-Agent Transformer (MAT) (Wen et al. Section 3 introduces the problem formulation and methods, while Section 4 presents and Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning Matteo Bettini 1Ryan Kortvelesy Amanda Prorok Abstract The study of behavioral diversity in Multi-Agent Reinforcement Learning (MARL) is a nascent yet promising field. While solutions for standardized reporting have been proposed to address the issue, we still lack a benchmarking tool that enables standardization and reproducibility, while leveraging cutting-edge Reinforcement Learning (RL) View PDF Abstract: Multi-agent reinforcement learning experiments and open-source training environments are typically limited in scale, supporting tens or sometimes up to hundreds of interacting agents. A reinforcement-learning agent is modeled to perform sequential decision-making by interacting with the environment. l. For each algorithm, we describe the possible Index Terms—multi-agent systems, reinforcement learning, game theory, distributed control. Littman Brown University / Bellcore Department of Computer Science Brown University Providence, RI 02912-1910 mlittman@cs. The first multi-agent rein-forcement learning approaches considered centralized policies where the states, actions and observations of every agent are globally Standard multi-agent reinforcement learning (MARL) algorithms are vulnerable to sim-to-real gaps. This re- quirement aligns with the commonly adopted MARL frame-work of Centralized Training with As of late, multi-agent reinforcement learning, a gen-eralization of single-agent reinforcement learning tasks, has been gaining momentum since it is aligned with the grow-ing attention on multi-agent systems and the applications thereof. The book can be ordered online Our approach extends the tradi-tional deep reinforcement learning algorithm by making use of stochastic policies during execution time and station-ary policies for homogenous agents View a PDF of the paper titled Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms, by Kaiqing Zhang and 2 other authors This paper reviews recent advances on a sub-area of multi-agent reinforcement learning: decentralized MARL with networked agents, and covers several of the research We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. A promising approach is to learn a meaningful latent representation space through auxiliary learning objectives alongside the MARL objective to aid in learning a successful control policy. At each step, agent interacts with the environment by B. Such multi-agent systems can be designed and developed using multi-agent reinforcement learning A MBC:Multi-Brain collaborative system that incorporates the concepts of Multi-Agent Reinforcement Learning and introduces collaboration between the Blind Policy and the Perceptive Policy improves the robot's passability and robustness against perception failures in complex environments, validating the effectiveness of multi-policy collaboration in enhancing Reinforcement learning (RL) has been an active research area in AI for many years. [39] compared the performance of cooperative agents to independent agents in reinforcement learning settings. Stragglers arise frequently in a distributed learning system, due to the existence of various system disturbances such as slow Multi-agent reinforcement learning with emergent com-munication (EC-MARL) is a promising solution to address high dimensional continuous control problems with partially observ-able states in a cooperative fashion where agents build an emer-gent communication protocol to solve complex tasks. In particular, deep RL Offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn optimal multi-agent policies from pre-collected datasets. . cn zongqing. MAT is an encoder of multi-agent reinforcement learning (MARL). On the other hand, humans readily form beliefs about the knowledge possessed by their peers and leverage beliefs to inform decision-making. Robust Multi-Agent Reinforcement Learning with Model Uncertainty Kaiqing Zhang \Tao Sun yYunzhe Tao Sahika Genc Sunil Mallyay Tamer Basar ¸ Department of ECE and CSL, University of Illinois at Urbana-Champaign yAmazon Web Services {kzhang66, basar1}@illinois. View PDF Abstract: Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI) technique. In particular, we have focused on five common approaches on modeling and solving cooperative multi View PDF HTML (experimental) Abstract: Sample efficiency remains a key challenge in multi-agent reinforcement learning (MARL). [13] later Coding for Distributed Multi-Agent Reinforcement Learning Baoqian Wang1 Junfei Xie2 Nikolay Atanasov3 Abstract—This paper aims to mitigate straggler effects in synchronous distributed learning for multi-agent reinforcement learning (MARL) problems. INTRODUCTION LARGE-SCALE autonomous driving systems have at-tracted tons of attention and millions of funding from industry, academia, and government in recent years [1], [2]. The aim of this review article is to provide an overview of recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms. nguyen,duy. However, the absence of standardized This paper presents a meta-modelling framework for committee machine based on simulation and investigation of multi-agent reinforcement hierarchical reinforcement learning inMulti-agent environment learning to cooperate inmulti-agent systems by combining traf?c light control by multiagent reinforcement learning multi- agent relational reinforcement learning View a PDF of the paper titled QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning, by Tabish Rashid and 5 other authors. However, it remains elusive how to design such algo-rithms with statistical guarantees. PettingZoo’s API Individual Reward Assisted Multi-Agent Reinforcement Learning For multi-agent policy gradient algorithms, many works (Burda et al. If agents act independently, greedy policies with respect to their local state-action value functions do not necessarily maximize the global value function. uk, t. 48550/arXiv. 12. The emergence of MARL marks a significant advancement in artificial intelligence, particularly in handling complex and dynamic environments with multiple interacting agents. Leveraging a multi-agent performance di erence Abstract—Multi-Agent Reinforcement Learning (MARL) algo-rithms are widely adopted in tackling complex tasks that require collaboration and competition among agents in dynamic Multi-Agent Systems (MAS). ,2021) adopt the Multi-Critic technique as a way of combining multi-ple rewards, which allows each agent to maintain different critics for different rewards and update the policy accord- View PDF Abstract: Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. cn Abstract Reinforcement learning (RL) is inspired by the way human infants and animals learn from the environment. The remainder of this paper is organized as follows: Section 2 reviews related work. This paper articulates the importance of EC-MARL within the context of future Multi-agent reinforcement learning (MARL) is emerging as an important tool for solving various sequential decision making problems in application areas like 5G networks, un-manned aerial vehicle (UAV) swarms, autonomous driving, power grid control, and Internet of things [Li et al. 2208. While the computation of many intrinsic rewards relies on es-timating variational posteriors using neural network approx-imators, a notable challenge has surfaced due to the limited expressive capability of these neural statistics approximators. tomilin@tue. View PDF Abstract: Following the remarkable success of the AlphaGO series, 2019 was a booming year that witnessed significant advances in multi-agent reinforcement learning (MARL) techniques. A central issue in the field is the formal statement of the multi-agent learning goal. Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI) technique. However, recent advances in multi-agent learning mainly focus on value decomposition while leaving entity interactions still intertwined, which easily leads to over PDF | Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. At each time step In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration. We tackle this problem by developing algo-rithms for multi-agent deep RL, in which multiple agents learn how to communicate and (inter-)act optimally to achieve a specified goal [1–6]. au Abstract—Deep learning View PDF Abstract: In multi-agent reinforcement learning, the problem of learning to act is particularly difficult because the policies of co-players may be heavily conditioned on information only observed by them. ) which are interacting within a common environment. cn Abstract In this paper, we study the problem of networked multi-agent reinforcement learn-ing (MARL), where a number of agents are This article explores the application of Multi-Agent Reinforcement Learning (MARL) combined with Proximal Policy Optimization (PPO) to optimize autonomous vehicle platooning. Although the multi-agent domain has been overshadowed by its single-agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address problems with real-world complexity. du@kcl. It is an interdisciplinary domain with a long history that Multi-Agent Reinforcement Learning Scalable learning of coordinated agent policies and inter-agent communi-cation in multi-agent systems is a long-standing open problem. In View PDF HTML (experimental) Abstract: The Transformer model has demonstrated success across a wide range of domains, including in Multi-Agent Reinforcement Learning (MARL) where the Multi-Agent Transformer (MAT) has emerged as a leading algorithm in the field. nahavandi}@deakin. However, as autonomous vehicles and vehicle-to-X communications become more mature, solutions that multi-agent environments with a universal, elegant Python API. 1. Large sequence model (SM) such as GPT A reinforcement learning (RL) agent learns by interact-ing with its environment, using a scalar reward signal as performance feedback [1]. The body of Anytime-Constrained Multi-Agent Reinforcement Learning Jeremy McMahan ∗Xiaojin Zhu November 1, 2024 Abstract Weintroduceanytimeconstraintstothemulti Markov games as a framework for multi-agent reinforcement learning Michael L. edu. This paper aims to review methods and applications and point out research trends and visionary prospects for the next decade. uk, meng. INTRODUCTION R EINFORCEMENT Learning (RL) has achieved rapid progress in cooperative and competitive multi-agent games, such as OpenAI Five[1] and AlphaStar[2]. The simplicity and generality of this setting make it attractive also for multi-agent learning. , 2022; Canese et al. To address this, distributionally robust Markov games (RMGs) have been proposed to enhance robust-ness in MARL by optimizing the worst-case performance when game dynamics shift within a prescribed uncertainty set. Wu1, Angelo Passaro3 1University of Central Florida, Orlando, FL 32816-2362, USA 2Aeronautics Institute of Technology, Sao Jos˜ e dos Campos, SP 12228-900, Brazil´ 3Institute for Advanced Studies, S˜ao Jos e dos Campos, SP In multi-agent reinforcement learning (MARL) (Yang and Wang, 2020), applying effective Q-learning based method is no longer straightforward. edu July 26, 2018 Abstract Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize an objective over the long term. Solving RMGs remains under-explored, from problem Keywords: Multi-agent Reinforcement Learning · Deep Reinforcement Learning · Human-Agent Teaming · Collaboration 1 Introduction Reinforcement learning (RL) is an attractive option for providing adaptive behavior in computational agents because of its theoretical generalizability to complex problem spaces [1, 2]. However, learning such tasks from scratch is arduous and may not always be feasible, particularly for MASs with a large number of interactive agents due to the extensive Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. Recently there has been growing interest in extending RL to the multi-agent domain. Each agent makes a decision in each time-step and works along with the other agent(s) to achieve Multi-agent reinforcement learning (MARL) is an important subfield in the community of machine learning. PDF | Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, | Find, read and cite all the research you need Keywords Reinforcement learning ·Multi-agent systems ·Cooperative learning 1Introduction Multi-Agent Reinforcement Learning (MARL) algorithms are dealing with systems consisting of several agents (robots, machines, cars, etc. However, the main challenge in multi-agent RL (MARL) is that each learning agent must explicitly consider other View PDF HTML (experimental) Abstract: The field of Multi-Agent Reinforcement Learning (MARL) is currently facing a reproducibility crisis. In this review article, we have focused on presenting recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms. Different viewpoints on this issue have led to the proposal of many different goals, among which two focal points can be distinguished: stability of the agents’ learning dynamics, and adaptation to the changing behavior of the other agents. Addressing this challenge, we introduce the regulatory hierarchical multi-agent coordination (RHMC), a hierarchical reinforcement learning approach. Multi-Agent Reinforcement Learning Yulai Zhao∗ Zhuoran Yang† Zhaoran Wang‡ Jason D. com Abstract In this work, we study the problem of multi Attention-Driven Multi-Agent Reinforcement Learning: Enhancing Decisions with Expertise-Informed Tasks Andre R. View PDF Abstract: In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. fang@liverpool. edu Abstract IntheMarkovdecisionprocess(MDP)formaliza-tion of reinforcement learning, a single adaptive agent interacts with an environment Index Terms—Multi-agent reinforcement learning, autonomous driving, artificial intelligence I. 1 Single-Agent RL. Either for solving a single-agent task or a multi-agent system, reinforcement learning entails the knowledge A novel architecture named Multi-Agent Transformer is introduced that effectively casts cooperative multi-agent reinforcement learning (MARL) into SM problems wherein the task is to map agents' observation sequence to agents' optimal action sequence and endows MAT with monotonic performance improvement guarantee. From the technical point of view,this has taken the community from the realm of Markov Decision Problems (MDPs) to the realm of game theory,and in particular stochastic (or Markov) games (SGs). This paper presents an overview of technical challenges in multi-agent learning as well as deep RL approaches to these challenges. nl 2 Center for Systems and Control, Delft University of Technology, The Netherlands, r. The motivation behind developing such a system is to replace human drivers with automated However, when multiple agents apply reinforcement learning in a shared environment, this might be beyond the MDP model. nl 3 Center for Systems and Control & Marine and Transport In the face of a series of challenging control tasks, multi-agent reinforcement learning has demonstrated its superiority. ,2019;Ye et al. szhz bljd tbqptdc kjwuz ezhf ebytyy gline spruh qyi jhgkp