scholarly journals Two-Stage Hybrid Network Clustering Using Multi-Agent Reinforcement Learning

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 232
Author(s):  
Joohyun Kim ◽  
Dongkwan Ryu ◽  
Juyeon Kim ◽  
Jae-Hoon Kim
Keyword(s):  
Reinforcement Learning ◽  
Delaunay Triangulation ◽  
Selection Process ◽  
Single Agent ◽  
Hybrid Approach ◽  
Critical Factor ◽  
Hybrid Network ◽  
Network Clustering ◽  
Two Stage ◽  
Multi Agent

In the Internet-of-Things (IoT) environments, the publish (pub)/subscribe (sub)-operated communication is widely employed. The use of pub/sub operation as a lightweight communication protocol facilitates communication among IoTs. The protocol consists of network nodes functioning as publishers, subscribers, and brokers, wherein brokers transfer messages from publishers to subscribers. Thus, the communication capability of the broker is a critical factor in the overall communication performance. In this study, multi-agent reinforcement learning (MARL) is applied to find the best combination of broker nodes. MARL goes through various combinations of broker nodes to find the best combination. However, MARL is inefficient to perform with an excessive number of broker nodes. Delaunay triangulation selects candidate broker nodes among the pool of broker nodes. The selection process operates as a preprocessing of the MARL. The suggested Delaunay triangulation is improved by the custom deletion method. Consequently, the two-stage hybrid approach outperforms any methods employing single-agent reinforcement learning (SARL). The MARL eliminates the performance fluctuation of the SARL caused by the iterative selection of broker nodes. Furthermore, the proposed approach requires a fewer number of candidate broker nodes and converges faster.

scholarly journals Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4948
Author(s):  
Lorenzo Canese ◽  
Gian Carlo Cardarilli ◽  
Luca Di Di Nunzio ◽  
Rocco Fazzolari ◽  
Daniele Giardino ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Models ◽  
Learning Algorithms ◽  
Single Agent ◽  
Critical Issues ◽  
Multi Agent ◽  
Pros And Cons ◽  
Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

scholarly journals On-Demand Channel Bonding in Heterogeneous WLANs: A Multi-Agent Deep Reinforcement Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2789 ◽  
Author(s):  
Hang Qi ◽  
Hao Huang ◽  
Zhiqun Hu ◽  
Xiangming Wen ◽  
Zhaoming Lu
Keyword(s):  
Reinforcement Learning ◽  
Transmission Rate ◽  
Single Agent ◽  
Time Of Day ◽  
Action Space ◽  
Traffic Load ◽  
Traffic Demand ◽  
Channel Bonding ◽  
On Demand ◽  
Multi Agent

In order to meet the ever-increasing traffic demand of Wireless Local Area Networks (WLANs), channel bonding is introduced in IEEE 802.11 standards. Although channel bonding effectively increases the transmission rate, the wider channel reduces the number of non-overlapping channels and is more susceptible to interference. Meanwhile, the traffic load differs from one access point (AP) to another and changes significantly depending on the time of day. Therefore, the primary channel and channel bonding bandwidth should be carefully selected to meet traffic demand and guarantee the performance gain. In this paper, we proposed an On-Demand Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) for heterogeneous WLANs to reduce transmission delay, where the APs have different channel bonding capabilities. In this problem, the state space is continuous and the action space is discrete. However, the size of action space increases exponentially with the number of APs by using single-agent DRL, which severely affects the learning rate. To accelerate learning, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is used to train O-DCB. Real traffic traces collected from a campus WLAN are used to train and test O-DCB. Simulation results reveal that the proposed algorithm has good convergence and lower delay than other algorithms.

Multi-agent reinforcement learning using ordinal action selection and approximate policy iteration

2016 ◽  
Vol 14 (06) ◽  
pp. 1650053
Author(s):  
Daxue Liu ◽  
Jun Wu ◽  
Xin Xu
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Action Selection ◽  
Policy Iteration ◽  
Common Interest ◽  
Policy Space ◽  
Markov Games ◽  
Approximate Policy Iteration ◽  
Multi Agent ◽  
Agent Coordination

Multi-agent reinforcement learning (MARL) provides a useful and flexible framework for multi-agent coordination in uncertain dynamic environments. However, the generalization ability and scalability of algorithms to large problem sizes, already problematic in single-agent RL, is an even more formidable obstacle in MARL applications. In this paper, a new MARL method based on ordinal action selection and approximate policy iteration called OAPI (Ordinal Approximate Policy Iteration), is presented to address the scalability issue of MARL algorithms in common-interest Markov Games. In OAPI, an ordinal action selection and learning strategy is integrated with distributed approximate policy iteration not only to simplify the policy space and eliminate the conflicts in multi-agent coordination, but also to realize the approximation of near-optimal policies for Markov Games with large state spaces. Based on the simplified policy space using ordinal action selection, the OAPI algorithm implements distributed approximate policy iteration utilizing online least-squares policy iteration (LSPI). This resulted in multi-agent coordination with good convergence properties with reduced computational complexity. The simulation results of a coordinated multi-robot navigation task illustrate the feasibility and effectiveness of the proposed approach.

Special Issue on New Trends in Reinforcement Learning

2009 ◽  
Vol 13 (6) ◽  
pp. 599-599
Author(s):  
Kazuteru Miyazaki ◽  
◽  
Keiki Takadama ◽  
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Large Contribution ◽  
Special Issue ◽  
Learning Classifier System ◽  
Wide Range ◽  
Important Approach ◽  
Directed Learning ◽  
Multi Agent ◽  
Institute Of Technology

Recently, the tailor-made system that grants an individual request has been recognized as the important approach. Such a system requires the ggoal-directed learningh through interaction between user and system, which is mainly addressed in greinforcement learningh domain. This special issue on gNew Trends in Reinforcement Learningh called for papers on the cuttingedge research exploring the goal-directed learning, which represents reinforcement learning. Many contributions were forthcoming, but we finally selected 12 works for publication. Although greinforcement learningh is included in the title of this special issue, the research works do not necessarily have to be on reinforcement learning itself, so long as the theme coincides with that of this special issue. In making our final selections, we gave special consideration to the kinds of research which can actively lead to new trends in reinforcement learning. Of the 12 papers in this special issue, the first four mainly deal with the expansion of the reinforcement learning method in single agent environments. These cover a broad range of research, from works based on dynamic programming to exploitation-oriented methods. The next two works deal with the Learning Classifier System (LCS), which applies the rule discovery mechanism to reinforcement learning. LCS is a technique with a long history, but for this issue, we were able to publish two theoretical works. We are also grateful to Prof. Toshio Fukuda, Nagoya University, and Prof. Kaoru Hirota, Tokyo Institute of Technology, the editors-in-chief, and the NASTEC 2008 conference staff for inviting us to guest-edit this Journal. The next four papers mainly deal with multi agent environments. We were able to draw from a wide range of research: from measuring interaction, through the expansion of techniques incorporating simultaneous learning, to research leading to application in multi agent environments. The last two contributions mainly deal with application. We publish one paper on exemplar generalization and another detailing the successful application to government bond trading. Each of these researches can be considered to be at the cutting-edge of reinforcement learning. We would like to end by saying that we hope this special issue constitutes a large contribution to the development of the field while holding a wide international appeal.

Plan-based reward shaping for multi-agent reinforcement learning

2016 ◽  
Vol 31 (1) ◽  
pp. 44-58 ◽  
Author(s):  
Sam Devlin ◽  
Daniel Kudenko
Keyword(s):  
Reinforcement Learning ◽  
Potential Function ◽  
Single Agent ◽  
Individual Agent ◽  
Reward Shaping ◽  
Multi Agent ◽  
Theoretical Results ◽  
Planning Knowledge

AbstractRecent theoretical results have justified the use of potential-based reward shaping as a way to improve the performance of multi-agent reinforcement learning (MARL). However, the question remains of how to generate a useful potential function.Previous research demonstrated the use of STRIPS operator knowledge to automatically generate a potential function for single-agent reinforcement learning. Following up on this work, we investigate the use of STRIPS planning knowledge in the context of MARL.Our results show that a potential function based on joint or individual plan knowledge can significantly improve MARL performance compared with no shaping. In addition, we investigate the limitations of individual plan knowledge as a source of reward shaping in cases where the combination of individual agent plans causes conflict.

scholarly journals Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns

2019 ◽  
Author(s):  
Yong Liu ◽  
Yujing Hu ◽  
Yang Gao ◽  
Yingfeng Chen ◽  
Changjie Fan
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Transfer ◽  
Value Function ◽  
Single Agent ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Markov Decision ◽  
Dimensional State Space ◽  
Multi Agent ◽  
Function Transfer

Many real-world problems, such as robot control and soccer game, are naturally modeled as sparse-interaction multi-agent systems. Reutilizing single-agent knowledge in multi-agent systems with sparse interactions can greatly accelerate the multi-agent learning process. Previous works rely on bisimulation metric to define Markov decision process (MDP) similarity for controlling knowledge transfer. However, bisimulation metric is costly to compute and is not suitable for high-dimensional state space problems. In this work, we propose more scalable transfer learning methods based on a novel MDP similarity concept. We start by defining the MDP similarity based on the N-step return (NSR) values of an MDP. Then, we propose two knowledge transfer methods based on deep neural networks called direct value function transfer and NSR-based value function transfer. We conduct experiments in image-based grid world, multi-agent particle environment (MPE) and Ms. Pac-Man game. The results indicate that the proposed methods can significantly accelerate multi-agent reinforcement learning and meanwhile get better asymptotic performance.

scholarly journals A Novel Heterogeneous Swarm Reinforcement Learning Method for Sequential Decision Making Problems

2019 ◽  
Vol 1 (2) ◽  
pp. 590-610
Author(s):  
Zohreh Akbari ◽  
Rainer Unland
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Single Agent ◽  
Sequential Decision Making ◽  
Multi Agent Systems ◽  
Sequential Decision ◽  
Agent Systems ◽  
Novel Approach ◽  
Markov Decision ◽  
Multi Agent

Sequential Decision Making Problems (SDMPs) that can be modeled as Markov Decision Processes can be solved using methods that combine Dynamic Programming (DP) and Reinforcement Learning (RL). Depending on the problem scenarios and the available Decision Makers (DMs), such RL algorithms may be designed for single-agent systems or multi-agent systems that either consist of agents with individual goals and decision making capabilities, which are influenced by other agent’s decisions, or behave as a swarm of agents that collaboratively learn a single objective. Many studies have been conducted in this area; however, when concentrating on available swarm RL algorithms, one obtains a clear view of the areas that still require attention. Most of the studies in this area focus on homogeneous swarms and so far, systems introduced as Heterogeneous Swarms (HetSs) merely include very few, i.e., two or three sub-swarms of homogeneous agents, which either, according to their capabilities, deal with a specific sub-problem of the general problem or exhibit different behaviors in order to reduce the risk of bias. This study introduces a novel approach that allows agents, which are originally designed to solve different problems and hence have higher degrees of heterogeneity, to behave as a swarm when addressing identical sub-problems. In fact, the affinity between two agents, which measures the compatibility of agents to work together towards solving a specific sub-problem, is used in designing a Heterogeneous Swarm RL (HetSRL) algorithm that allows HetSs to solve the intended SDMPs.

Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies

2014 ◽  
Author(s):  
Kallirroi Georgila ◽  
Claire Nelson ◽  
David Traum
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Multi Agent

scholarly journals Reinforcement Learning under Threats

2019 ◽  
Vol 33 ◽  
pp. 9939-9940 ◽  
Author(s):  
Victor Gallego ◽  
Roi Naveiro ◽  
David Rios Insua
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Potential Threat ◽  
Q Learning ◽  
Learning Framework ◽  
Opponent Modeling ◽  
Theoretical Approaches ◽  
New Learning ◽  
Markov Decision ◽  
Multi Agent

In several reinforcement learning (RL) scenarios, mainly in security settings, there may be adversaries trying to interfere with the reward generating process. However, when non-stationary environments as such are considered, Q-learning leads to suboptimal results (Busoniu, Babuska, and De Schutter 2010). Previous game-theoretical approaches to this problem have focused on modeling the whole multi-agent system as a game. Instead, we shall face the problem of prescribing decisions to a single agent (the supported decision maker, DM) against a potential threat model (the adversary). We augment the MDP to account for this threat, introducing Threatened Markov Decision Processes (TMDPs). Furthermore, we propose a level-k thinking scheme resulting in a new learning framework to deal with TMDPs. We empirically test our framework, showing the benefits of opponent modeling.

scholarly journals Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 123 ◽  
Author(s):  
Xiaohan Fang ◽  
Jinkuan Wang ◽  
Guanru Song ◽  
Yinghua Han ◽  
Qiang Zhao ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
New Paradigm ◽  
Agent Based ◽  
Vehicle To Grid ◽  
Market Participants ◽  
Home Energy Management ◽  
Market Operation ◽  
Multi Agent ◽  
Home Energy Management System

Residential microgrid is widely considered as a new paradigm of the home energy management system. The complexity of Microgrid Energy Scheduling (MES) is increasing with the integration of Electric Vehicles (EVs) and Renewable Generations (RGs). Moreover, it is challenging to determine optimal scheduling strategies to guarantee the efficiency of the microgrid market and to balance all market participants’ benefits. In this paper, a Multi-Agent Reinforcement Learning (MARL) approach for residential MES is proposed to promote the autonomy and fairness of microgrid market operation. First, a multi-agent based residential microgrid model including Vehicle-to-Grid (V2G) and RGs is constructed and an auction-based microgrid market is built. Then, distinguish from Single-Agent Reinforcement Learning (SARL), MARL can achieve distributed autonomous learning for each agent and realize the equilibrium of all agents’ benefits, therefore, we formulate an equilibrium-based MARL framework according to each participant’ market orientation. Finally, to guarantee the fairness and privacy of the MARL process, we proposed an improved optimal Equilibrium Selection-MARL (ES-MARL) algorithm based on two mechanisms, private negotiation and maximum average reward. Simulation results demonstrate the overall performance and efficiency of proposed MARL are superior to that of SARL. Besides, it is verified that the improved ES-MARL can get higher average profit to balance all agents.

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