multi agent reinforcement learning for networked system control

2013. Mach. Transp. A Review of Safe Reinforcement Learning: Methods, Theory and Applications. [CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2004.01339/code), deep reinforcement learning, multi-agent reinforcement learning, decision and control, This paper proposes a new formulation and a new communication protocol for networked multi-agent control problems. Y. Wang This review covers several of our research endeavors in this direction, as well as progress made by other researchers along the line. Equilibrium and dynamics of local voltage control in distribution systems. Although the global reward design outperforms the local reward design before 2000 epochs, the variance of the global reward is relatively large. The pseudo-code of the proposed MA2C algorithm is shown in Algorithm 1. Transp. A data-driven multi-agent autonomous voltage control framework using deep reinforcement learning. More specifically, distributed generation is deployed in the network demanding decentralised control mechanisms to ensure reliable power system operations. Yuanqi Gao, Wei Wang, and Nanpeng Yu. Comput.-Aided Civ. As HDVs bring unknown/uncertain behaviors, planning, and control in such mixed traffic to realize safe and efficient maneuvers is a challenging task [4]. Abstract. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. We set the safety factor \(\omega _{s}\) to a large value, because safety is the most important criterion during driving. Then we present the proposed multi-agent actor-critic algorithm, featuring a parameter-sharing mechanism and efficient reward function design, to solve the formulated POMDP. First define all hyperparameters (including algorithm and DNN structure) in a config file under [config_dir] (examples), and create the base directory of each experiement [base_dir]. Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, Yaodong Yang, and Alois Knoll. A parameter-sharing scheme is exploited to foster inter-agent collaborations. 355. We have re-run the ATSC experiments with SUMO 1.2.0 using the master code, and provided the following training plots as reference. [2108.00506] Scalable Multi-agent Reinforcement Learning Algorithm for Transp. Abstract: This paper considers multi-agent reinforcement learning (MARL) in networked system control. It is clear that the proposed algorithm achieves scalable and stable performance whenever the HDVs take aggressive or courteous behaviors. With the increasing penetration of renewable and distributed energy sources in the grid, growing complexity, increasing uncertainty, and aggravating volatility bring great challenges to voltage control in modern power systems. 18(5), 10871096 (2016), Y. Chen, C. Dong, P. Palanisamy, P. Mudalige, K. Muelling, J.M. A theory of cerebellar function. Multi-agent Reinforcement Learning for Networked System Control Such a problem is more serious in mixed-traffic scenarios with unknown or stochastic behaviors of drivers. In actor-critic (A2C) algorithms [27], a critic network, parameterized by , learns the state-value function \(V_{\omega }^{\pi _{\theta }}(s_{t})\) and an actor network \(\pi _{\theta }(a_{t}{\mid }s_{t})\) parameterized by is applied to update the policy distribution in the direction suggested by the critic network as follows: where the advantage function \(A_{t}= Q^{\pi _{\theta }}(s,a) - V_{\omega }^{\pi _{\theta }}(s_{t})\) [27] is introduced to reduce the sample variance. Multi-Agent Deep Reinforcement Learning in 13 Lines of Code Using multi-agent systems; . The reason is that, in separate actor-critic networks, the critic network can only guide the actor network to the correct training direction until the critic network is well-trained which may take a long time to achieve. Reward Function: Multiple metrics including safety, traffic efficiency, and passengers comfort are considered in the reward function design: safety evaluation \(r_{s}\): The vehicle should operate without collisions. Open Peer Review. ATSC Grid: Adaptive traffic signal control in a synthetic traffic grid. This review covers several of our research endeavors in this direction, as well as progress made by other researchers along the line. 59, 6 (2014), 1524--1538. 8(b) and Fig. However, when seeking to use RL in the context of the control and optimization of large-scale networked systems, scalability quickly becomes an issue. Dive into the research topics of 'Decentralized multi-agent reinforcement learning with networked agents: recent advances'. Google Scholar, S. Shalev-Shwartz, Shaked Shammah, and Amnon Shashua. We use cookies to ensure that we give you the best experience on our website. Multi-agent safe policy learning for power management of networked microgrids. Shapley Q-value: A local reward approach to solve global reward games. 1 , To overcome the communication overhead and the credit assignment problem [34], we adopt the local reward design [23] as follows: where \(\mid \nu _{i} \mid \) denotes the cardinality of a set containing the ego vehicle and its close neighbors. Autonomous agents and multiagent systems. Res. P. Dayan The MARL algorithms have also been applied to autonomous driving [1316], with the objective of accomplishing autonomous driving tasks cooperatively and reacting timely to HDVs. In this subsection, we attempt to interpret the learned AVs behavior. Astrophysical Observatory. We then propose our approach to extending deep reinforcement learning to multi-agent sys-tems. Her research interests lie in the modeling, evaluation, and control of large-scale dynamical networks, cyber-physical system and stochastic networks. Meeting, 2004, DKS Associates. 2016. M. McDonald , As shown in Fig. GitHub - cts198859/deeprl_network: multi-agent deep reinforcement 43(1), 4770 (2009), J. Nilsson, J. Silvlin, M. Brannstrom, E. Coelingh, J. Fredriksson, If, when, and how to perform lane change maneuvers on highways. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Open Publishing. arXiv preprint arXiv:1704.01212, 2017. One corechallengeisscalability. The vehicle control sampling frequency is set as the default value of 5Hz. traffic signal scheduling, Subjects: "Learning to communicate with deep multi-agent reinforcement learning." RL is a powerful tool for decision-making in complex and stochastic environments. Multi-agent reinforcement learning for cooperative lane - Springer A tag already exists with the provided branch name. His work won the Best Student Paper Award at the 2017 ACM Hybrid Systems: Computation and Control Conference, the Best Paper Award finalist at the 2018 ACM . In addition, frequent lane changes should be avoided. 2021. Advances in neural information processing systems, Vol. IEEE, 4329--4334. In this paper we show the application of multi-agent modeling and simulation with distributed reinforcement learning to one of the major problems in power system operations, i.e. Johannes Ackermann, Volker Gabler, Takayuki Osa, and Masashi Sugiyama. arXiv preprint arXiv:1802.08757, 2018. Self-organizing traffic lights. arXiv:2010.05436, P. Palanisamy, Multi-agent connected autonomous driving using deep reinforcement learning, in International Joint Conference on Neural Networks (IJCNN) (2020), pp. CACC Slow-down: Cooperative adaptive cruise control for following the leading vehicle to slow down. Rev. Multi-agent actor-critic for mixed cooperative-competitive environments. He was an assistant professor at the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech until 2018. Graph neural network and multi-agent reinforcement learning for machine Illustration of the considered lane-changing scenario (green: AVs, blue: HDVs, arrow curve: a possible trajectory of the ego vehicle AV1 to make the lane change). CoRR. (2004) The considered lane-changing scenario is illustrated in Fig. Walkins , We rst online how deep reinforcement learning works in single agent systems. He obtained his Bachelors Degree in Physics/EE and MSEE at Rice University, his MS in Aeronautical Engineering from Univ. C. Barnhart , Recent development of (single-agent) deep reinforcement learning has created a resurgence of interest in developing new MARL algorithms, especially those founded on theoretical analysis. L.P. Kaelbling , Yuanyuan Shi, Guannan Qu, Steven Low, Anima Anandkumar, and Adam Wierman. Specifically, the proposed algorithm in this paper is based on communication network (CommNet) method utilizing centralized training and distributed execution (CTDE) in multiple UAMs for providing efficient air transportation services to passengers collaboratively. The goal of this paper is to develop scalable multi-agent RL for networked systems. D.S. Multi-agent reinforcement learning (MARL) has long been a significant research topic in both machine learning and control systems. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. Learn. 284, The Institution of Engineering and Technology is registered as a Charity in England & Wales (no 211014) and Scotland (no SC038698). systems. 2017. , This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 264 (2020), 114772. headway evaluation \(r_{d}\): The vehicle should maintain a safe distance from the preceding vehicles during driving to avoid collisions. PP(99), 19 (2021), R. Du, S. Chen, Y. Li, J. Dong, P.Y.J. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. Inspec keywords: I. Elhanany , 17th Int. Analysis of intersection delay under real-time adaptive signal control. Michael L Littman. . CoRR. 1 , Experimental results clearly demonstrate the advantages of multi-agent RL-based control over LQF governed isolated single-intersection control, thus paving the way for efficient distributed traffic signal control in complex settings. [1911.10635] Multi-Agent Reinforcement Learning: A Selective Overview arXiv:2010.05437, D. Chen, Z. Li, Y. Wang, L. Jiang, Y. Wang, Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic (2021). Res. 2013. IEEE Trans. Collective Learning in a multi-agent system is challenging. It includes the longitudinal position x, the lateral position y of the observed vehicle relative to the ego vehicle, the longitudinal speed \(v_{x}\), and the lateral speed \(v_{y}\) of the observed vehicle relative to the ego vehicle. However, the majority of those studies are focused on a single-vehicle setting, and lane-changing in the context of multiple AVs coexisting with human-driven vehicles (HDVs) have received scarce attention. 34 (2021), 3271--3284. arXiv preprint arXiv:1702.08887, 2017. We therefore adopt a deep multi-agent actor-critic method which uses a \emph{centralised but factored critic} to rehearse coordination ahead of execution. 508 (04 2014), 309--11. https://doi.org/10.1038/508309a. : J. Transp. 4 , In Sukthankar G., RodriguezAguilar J. Distributed constrained optimization by consensus-based primal-dual perturbation method. In this section, we evaluate the performance of the proposed MARL algorithm in terms of training efficiency, safety, and driving comfort in the considered highway lane changing scenario shown in Fig. Dolan, Attention-based hierarchical deep reinforcement learning for lane change behaviors in autonomous driving, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2019), pp. He received the Diploma in Electronic and Computer Engineering from the Technical University of Crete, Greece in 2006. Conf. Deep multi-agent reinforcement learning for decentralized continuous cooperative control. Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, and Tim C Green. Syst. Edit social preview. If the agent can only observe a part of the state \(s_{t}\), the underlying dynamics becomes a POMDP [26] and the goal is then to learn a policy that maps from the partial observation to an appropriate action to maximize the rewards. IEEE Transactions on Smart Grid, Vol. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. . 2017. S. Hoogendoorn , Dr. Cansever has also worked at Johns Hopkins University Applied Physics Laboratory, AT&T Bell Labs, and GTE Laboratory. Intell. Figure6 shows the training performance of two different HDV models (i.e., aggressive or politeness) under different traffic densities. 12, 4 (2021), 3594--3604. The main contributions and the technical advancements of this paper are summarized as follows. Albus where \(v_{t}\), \(v_{\min }\) and \(v_{\max }\) are the current, minimum, and maximum speeds of the ego vehicle, respectively. B. Paden, M. Cp, S.Z. 21, 4 (2008), 682--697. Two types of agents, a central agent and an outbound agent, were employed. R.S. . K. Aboudolas Eng. In this paper, we review recent advances on a sub-area of this topic: decentralized MARL with networked agents. . 2017. 387414, V. Mnih, A.P. CoRR. 321 - In this work, a Multi-Agent Reinforcement Learning approach is proposed . Wei Wang, Nanpeng Yu, Jie Shi, and Yuanqi Gao. J. Xin Chen, Guannan Qu, Yujie Tang, Steven Low, and Na Li. Advances in Neural Information Processing Systems. Theodore J Perkins and Andrew G Barto. Google Scholar, M. Kaushik, S. Phaniteja, K.M. Jun Yan. Multi-agent Reinforcement Learning for Networked System Control HY helped revise the manuscript. IEEE Transactions on Smart Grid, Vol. In this paper, we formalize it with a constrained RL formulation in the distributional RL setting. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and the smart grid. Haonan Yu, Wei Xu, and Haichao Zhang. 120 - The first approach is centralized reinforcement learning (CRL) [25, 28], where a centralized network is used to share the experiences of the robots, which in return helps to optimize the actions of those agents.This approach is not scalable, as a joint dimension . It takes vehicle acceleration as the input variable of the model and can work well with most car-following models. IEEE Robot. A novel signal scheduling algorithm with quality of service provisioning for an isolate intersection. We hope that this review promotes additional research efforts in this exciting yet challenging area. The coordination of distributed resources such as electric vehicles and heating will be critical to the successful integration of large shares of renewable energy in our electricity grid and, thus, to help mitigate climate change. Using reinforcement learning to control multiple agents, unsurprisingly, is referred to as multi-agent reinforcement learning. Similarly, the state-value function is defined as \(V^{\pi }(s_{t}) = E_{\pi }{[R_{t}{\mid }{s=s_{t}}]}\) representing the expected return for following the policy from state \(s_{t}\). Lett. Part C By evaluating the performance of the proposed algorithm in data-intensive simulations, the results show that the proposed algorithm outperforms existing approaches in terms of air transportation service quality. PMLR, 1889--1897. 53 - Board Furthermore, this paper adopts actual vertiport maps and UAM specifications for constructing realistic air transportation networks. PDF Abstract 53. Networked Multi-Agent Reinforcement Learning in Continuous Spaces multiintersection vehicular networks; Performance comparisons of acceleration between the reward design with or without comfort measurement. . IEEE We evaluate each model 3 times every 200 training episodes. Krishna, Parameter sharing reinforcement learning architecture for multi agent driving behaviors (2018). The simulation environment is modified from the gym-based highway-env simulator[38]. Intell. In addition, Minimize Overall Braking Induced By Lane Change model (MOBIL)[37] is adopted for the lateral control. DeepMind AI Reduces Google Data Centre Cooling Bill by 40%. Complex Syst. . 1. Meanwhile, the ego vehicle continues to slow down to ensure a safe headway distance with ego vehicle as shown in Fig. M. Papageorgiou , 2014. Chan, M. Liu, C. Zhu, W. Lu, K. Hu, A cooperative lane change model for connected and automated vehicles. In this subsection, we evaluate the effectiveness of the proposed multi-objective reward function with the driving comfort in Eqn. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and the smart grid. "Stabilising experience replay for deep multi-agent reinforcement learning." 8(4), 6878 (2016), J. Nilsson, M. Brnnstrm, E. Coelingh, J. Fredriksson, Lane change maneuvers for automated vehicles. Multi-agent reinforcement learning (MARL) has long been a significant research topic in both machine learning and control systems. The Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, USA, You can also search for this editor in Proximal policy optimization algorithms. Training config/data and the trained model will be output to [base_dir]/data and [base_dir]/model, respectively. This paper considers multi-agent reinforcement learning (MARL) in networked system control. Eng. This assumption is valid in most networked control systems such astrafc and wireless networks, as well as the power grid, where the impact of each agent is spread over the . 2014. Riedmiller, Playing atari with deep reinforcement learning (2013). . A challenging application of artificial intelligence systems involves the scheduling of traffic signals in multi-intersection vehicular networks. CoRR. Wiering, M.: `Multi-agent reinforcement learning for traffic light control', Proc. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 21482155, O. Vinyals, I. Babuschkin, W.M. Multi-agent Reinforcement Learning for Networked System Control 270 - Smart grid technologies: Communication technologies and standards. To address the above issues, we develop a multi-agent reinforcement learning algorithm by employing a multi-agent advantage actor-critic network (MA2C) for multi-AV lane-changing decision making, featuring a novel local reward design that incorporates the safety, efficiency, and passenger comfort as well as a parameter sharing scheme to foster . arXiv (2021). 14541460, C. Xi, T. Shi, Y. Wu, L. Sun, Efficient motion planning for automated lane change based on imitation learning and mixed-integer optimization, in 23rd International Conference on Intelligent Transportation Systems (ITSC) (2020), pp. Intell. Notice, Smithsonian Terms of Syst. Electronic Imaging, Vol. The Proposed MA2C: This is our proposed method with the designed multi-objective reward function design, parameter sharing, and local reward design schemes. His research interests include reinforcement learning, control theory, and safe/assured autonomy. We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent. Neural networks: a comprehensive foundation. Zhou, W., Chen, D., Yan, J. et al. IEEE Transactions on Smart Grid, Vol. The latter is aimed at minimising the average delay, congestion and likelihood of intersection . In the field of computer intelligence, it has always been a challenge to construct an agent model that can be adapted to various complex tasks. J. Artif. Lowe, Ryan, et al. . In particular, the MARL methods [15, 17] have been applied to highway lane change tasks and show promising and scalable performance, in which AVs learn cooperatively via sharing the same objective (i.e., reward/cost function) that considers safety and efficiency. https://www.deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40 Retrieved October 15, 2022 from. 7, 4 (2011), 529--539. A. Kotsialos , 263 - In this research some agents in the power network work Safety in reinforcement learning (RL) is a key property in both training and execution in many domains such as autonomous driving or finance. arXiv preprint arXiv:1801.08757 (2018). 2017. We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to . Edit social preview. C. Diakaki , Use, Smithsonian 2011. In model-free RL methods, the policy is often represented by a neural network denoted as \(\pi _{\theta }(a_{t}{\mid }s_{t})\), where is the learnable parameters. In 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). UR - http://www.scopus.com/inward/record.url?scp=85111089577&partnerID=8YFLogxK, UR - http://www.scopus.com/inward/citedby.url?scp=85111089577&partnerID=8YFLogxK, JO - Frontiers of Information Technology and Electronic Engineering, JF - Frontiers of Information Technology and Electronic Engineering, Powered by Pure, Scopus & Elsevier Fingerprint Engine 2023 Elsevier B.V, We use cookies to help provide and enhance our service and tailor content. Kyriakos G. Vamvoudakis serves as an Assistant Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. Nature 575(7782), 350354 (2019), T. Chu, J. Wang, L. Codec, Z. Li, Multi-agent deep reinforcement learning for large-scale traffic signal control. 61. Part C CACC Catch-up: Cooperative adaptive cruise control for catching up the leadinig vehicle. Syst. 2021. Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. There is a little in-depth study on safe multi-agent policy learning in multi-agent-based voltage control, especially the direct correction of unsafe actions. RNN is used to extract more state information from the local . Open Access. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD '22). The parameters of the state-value function are then updated by minimizing the following loss function: where \(\mathcal{B}\) is the experience replay buffer that stores previously encountered trajectories and \(\omega '\) denotes the parameters of the target network [25]. , In this scenario, multiple agents perform sequential decision-making in a common environment, and without the coordination of any central controller, while being allowed to exchange information with their neighbors over a communication network. Advances in Neural Information Processing Systems, 2017. For ATSC Grid, please call build_file.py to generate SUMO network files before training. Vehbi C Gungor, Dilan Sahin, Taskin Kocak, Salih Ergut, Concettina Buccella, Carlo Cecati, and Gerhard P Hancke. Results show that coordination is achieved at scale, with minimal information and communication infrastructure requirements, no interference with daily activities, and privacy protection. The pre-learning of individual reinforcement learning policies can enable distributed control with no sharing of personal data required during execution. distributed algorithms for optimization and control [16, 1, 32], and has proven effective for designing 2020b. Joris Dinneweth, Abderrahmane Boubezoul, Stphane Espi, Xiaotong Nie, Yupeng Liang & Kazuhiro Ohkura, Zhimin Qiao, Liangjun Ke & Xiaoqiang Wang, Tomohiro Harada, Johei Matsuoka & Kiyohiko Hattori, I. K. Minashina, R. A. Gorbachev & E. M. Zakharova, Autonomous Intelligent Systems Ahmad EL Sallab, Mohammed Abdou, Etienne Perot, and Senthil Yogamani. feedforward neural network; Minrui Wang, Mingxiao Feng, Wengang Zhou, and Houqiang Li. Ha, S. Labi, A cooperative control framework for CAV lane change in a mixed traffic environment (2020). Evolutionary Reinforcement Learning: A Survey | Intelligent Computing 2019. In this paper, we review recent advances on a sub-area of this topic: decentralized MARL with networked agents. 35, 5 (2020), 4120--4123. She has been recognized by several prestigious awards, including the NSF CAREER Award, RTCA William E. Jackson Award and U.S. Ignite and GENI demonstration awards. 9. 3. PDF Multi-Agent Deep Reinforcement Learning - Stanford University University of Science and Technology of China, Hefei, China. Multi-agent reinforcement learning for active voltage control on power distribution networks. Recent years have witnessed significant advances in reinforcement learning (RL), which has registered great success in solving various sequential decision-making problems in machine learning. In addition, the performance gaps are enlarged as the number of vehicles increases.

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