Scaling Reinforcement Learning To The Unconstrained Multi Agent Domain

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Scaling Reinforcement Learning to the Unconstrained Multi-agent Domain

Author : Victor Palmer
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (61 download)

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Book Synopsis Scaling Reinforcement Learning to the Unconstrained Multi-agent Domain by : Victor Palmer

Download or read book Scaling Reinforcement Learning to the Unconstrained Multi-agent Domain written by Victor Palmer and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning is a machine learning technique designed to mimic the way animals learn by receiving rewards and punishment. It is designed to train intelligent agents when very little is known about the agent's environment, and consequently the agent's designer is unable to hand-craft an appropriate policy. Using reinforcement learning, the agent's designer can merely give reward to the agent when it does something right, and the algorithm will craft an appropriate policy automatically. In many situations it is desirable to use this technique to train systems of agents (for example, to train robots to play RoboCup soccer in a coordinated fashion). Unfortunately, several significant computational issues occur when using this technique to train systems of agents. This dissertation introduces a suite of techniques that overcome many of these difficulties in various common situations. First, we show how multi-agent reinforcement learning can be made more tractable by forming coalitions out of the agents, and training each coalition separately. Coalitions are formed by using information-theoretic techniques, and we find that by using a coalition-based approach, the computational complexity of reinforcement-learning can be made linear in the total system agent count. Next we look at ways to integrate domain knowledge into the reinforcement learning process, and how this can significantly improve the policy quality in multi-agent situations. Specifically, we find that integrating domain knowledge into a reinforcement learning process can overcome training data deficiencies and allow the learner to converge to acceptable solutions when lack of training data would have prevented such convergence without domain knowledge. We then show how to train policies over continuous action spaces, which can reduce problem complexity for domains that require continuous action spaces (analog controllers) by eliminating the need to finely discretize the action space. Finally, we look at ways to perform reinforcement learning on modern GPUs and show how by doing this we can tackle significantly larger problems. We find that by offloading some of the RL computation to the GPU, we can achieve almost a 4.5 speedup factor in the total training process.

Transfer Learning for Multiagent Reinforcement Learning Systems

Author : Felipe Leno da Silva
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1636391354
Total Pages : 131 pages
Book Rating : 4.6/5 (363 download)

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Book Synopsis Transfer Learning for Multiagent Reinforcement Learning Systems by : Felipe Leno da Silva

Download or read book Transfer Learning for Multiagent Reinforcement Learning Systems written by Felipe Leno da Silva and published by Morgan & Claypool Publishers. This book was released on 2021-05-27 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment. However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning. This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools. This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.

Scaling Multiagent Reinforcement Learning

Author : Scott Proper
Publisher :
ISBN 13 :
Total Pages : 246 pages
Book Rating : 4.:/5 (491 download)

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Book Synopsis Scaling Multiagent Reinforcement Learning by : Scott Proper

Download or read book Scaling Multiagent Reinforcement Learning written by Scott Proper and published by . This book was released on 2010 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity or "outcome space" explosion. Multiagent domains are particularly susceptible to these problems. This thesis describes ways to mitigate these curses in several different multiagent domains, including real-time delivery of products using multiple vehicles with stochastic demands, a multiagent predator-prey domain, and a domain based on a real-time strategy game. To mitigate the problem of state-space explosion, this thesis present several approaches that mitigate each of these curses. "Tabular linear functions" (TLFs) are introduced that generalize tile-coding and linear value functions and allow learning of complex nonlinear functions in high-dimensional state-spaces. It is also shown how to adapt TLFs to relational domains, creating a "lifted" version called relational templates. To mitigate the problem of action-space explosion, the replacement of complete joint action space search with a form of hill climbing is described. To mitigate the problem of outcome space explosion, a more efficient calculation of the expected value of the next state is shown, and two real-time dynamic programming algorithms based on afterstates, ASH-learning and ATR-learning, are introduced. Lastly, two approaches that scale by treating a multiagent domain as being formed of several coordinating agents are presented. "Multiagent H-learning" and "Multiagent ASH-learning" are described, where coordination is achieved through a method called "serial coordination". This technique has the benefit of addressing each of the three curses of dimensionality simultaneously by reducing the space of states and actions each local agent must consider. The second approach to multiagent coordination presented is "assignment-based decomposition", which divides the action selection step into an assignment phase and a primitive action selection step. Like the multiagent approach, assignment-based decomposition addresses all three curses of dimensionality simultaneously by reducing the space of states and actions each group of agents must consider. This method is capable of much more sophisticated coordination. Experimental results are presented which show successful application of all methods described. These results demonstrate that the scaling techniques described in this thesis can greatly mitigate the three curses of dimensionality and allow solutions for multiagent domains to scale to large numbers of agents, and complex state and outcome spaces.

Coordination of Large-Scale Multiagent Systems

Author : Paul Scerri
Publisher : Springer Science & Business Media
ISBN 13 : 0387279725
Total Pages : 343 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Coordination of Large-Scale Multiagent Systems by : Paul Scerri

Download or read book Coordination of Large-Scale Multiagent Systems written by Paul Scerri and published by Springer Science & Business Media. This book was released on 2006-03-14 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Challenges arise when the size of a group of cooperating agents is scaled to hundreds or thousands of members. In domains such as space exploration, military and disaster response, groups of this size (or larger) are required to achieve extremely complex, distributed goals. To effectively and efficiently achieve their goals, members of a group need to cohesively follow a joint course of action while remaining flexible to unforeseen developments in the environment. Coordination of Large-Scale Multiagent Systems provides extensive coverage of the latest research and novel solutions being developed in the field. It describes specific systems, such as SERSE and WIZER, as well as general approaches based on game theory, optimization and other more theoretical frameworks. It will be of interest to researchers in academia and industry, as well as advanced-level students.

Scaling Multi-agent Learning in Complex Environments

Author : Chongjie Zhang
Publisher :
ISBN 13 :
Total Pages : 194 pages
Book Rating : 4.:/5 (786 download)

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Book Synopsis Scaling Multi-agent Learning in Complex Environments by : Chongjie Zhang

Download or read book Scaling Multi-agent Learning in Complex Environments written by Chongjie Zhang and published by . This book was released on 2011 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cooperative multi-agent systems (MAS) are finding applications in a wide variety of domains, including sensor networks, robotics, distributed control, collaborative decision support systems, and data mining. A cooperative MAS consists of a group of autonomous agents that interact with one another in order to optimize a global performance measure. A central challenge in cooperative MAS research is to design distributed coordination policies. Designing optimal distributed coordination policies offline is usually not feasible for large-scale complex multi-agent systems, where 10s to 1000s of agents are involved, there is limited communication bandwidth and communication delay between agents, agents have only limited partial views of the whole system, etc. This infeasibility is either due to a prohibitive cost to build an accurate decision model, or a dynamically evolving environment, or the intractable computation complexity. This thesis develops a multi-agent reinforcement learning paradigm to allow agents to effectively learn and adapt coordination policies in complex cooperative domains without explicitly building the complete decision models. With multi-agent reinforcement learning (MARL), agents explore the environment through trial and error, adapt their behaviors to the dynamics of the uncertain and evolving environment, and improve their performance through experiences. To achieve the scalability of MARL and ensure the global performance, the MARL paradigm developed in this thesis restricts the learning of each agent to using information locally observed or received from local interactions with a limited number of agents (i.e., neighbors) in the system and exploits non-local interaction information to coordinate the learning processes of agents. This thesis develops new MARL algorithms for agents to learn effectively with limited observations in multi-agent settings and introduces a low-overhead supervisory control framework to collect and integrate non-local information into the learning process of agents to coordinate their learning. More specifically, the contributions of already completed aspects of this thesis are as follows: Multi-Agent Learning with Policy Prediction: This thesis introduces the concept of policy prediction and augments the basic gradient-based learning algorithm to achieve two properties: best-response learning and convergence. The convergence property of multi-agent learning with policy prediction is proven for a class of static games under the assumption of full observability. MARL Algorithm with Limited Observability: This thesis develops PGA-APP, a practical multi-agent learning algorithm that extends Q-learning to learn stochastic policies. PGA-APP combines the policy gradient technique with the idea of policy prediction. It allows an agent to learn effectively with limited observability in complex domains in presence of other learning agents. The empirical results demonstrate that PGA-APP outperforms state-of-the-art MARL techniques in both benchmark games. MARL Application in Cloud Computing: This thesis illustrates how MARL can be applied to optimizing online distributed resource allocation in cloud computing. Empirical results show that the MARL approach performs reasonably well, compared to an optimal solution, and better than a centralized myopic allocation approach in some cases. A General Paradigm for Coordinating MARL: This thesis presents a multi-level supervisory control framework to coordinate and guide the agents' learning process. This framework exploits non-local information and introduces a more global view to coordinate the learning process of individual agents without incurring significant overhead and exploding their policy space. Empirical results demonstrate that this coordination significantly improves the speed, quality and likelihood of MARL convergence in large-scale, complex cooperative multi-agent systems. An Agent Interaction Model: This thesis proposes a new general agent interaction model. This interaction model formalizes a type of interactions among agents, called {\em joint-even-driven} interactions, and define a measure for capturing the strength of such interactions. Formal analysis reveals the relationship between interactions between agents and the performance of individual agents and the whole system. Self-Organization for Nearly-Decomposable Hierarchy: This thesis develops a distributed self-organization approach, based on the agent interaction model, that dynamically form a nearly decomposable hierarchy for large-scale multi-agent systems. This self-organization approach is integrated into supervisory control framework to automatically evolving supervisory organizations to better coordinating MARL during the learning process. Empirically results show that dynamically evolving supervisory organizations can perform better than static ones. Automating Coordination for Multi-Agent Learning: We tailor our supervision framework for coordinating MARL in ND-POMDPs. By exploiting structured interaction in ND-POMDPs, this tailored approach distributes the learning of the global joint policy among supervisors and employs DCOP techniques to automatically coordinate distributed learning to ensure the global learning performance. We prove that this approach can learn a globally optimal policy for ND-POMDPs with a property called groupwise observability.

Handbook of Reinforcement Learning and Control

Author : Kyriakos G. Vamvoudakis
Publisher : Springer Nature
ISBN 13 : 3030609901
Total Pages : 833 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Handbook of Reinforcement Learning and Control by : Kyriakos G. Vamvoudakis

Download or read book Handbook of Reinforcement Learning and Control written by Kyriakos G. Vamvoudakis and published by Springer Nature. This book was released on 2021-06-23 with total page 833 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. 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. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.

Multi-Agent Coordination

Author : Arup Kumar Sadhu
Publisher : John Wiley & Sons
ISBN 13 : 1119699037
Total Pages : 320 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Multi-Agent Coordination by : Arup Kumar Sadhu

Download or read book Multi-Agent Coordination written by Arup Kumar Sadhu and published by John Wiley & Sons. This book was released on 2020-12-03 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.

Cooperation and Communication in Multiagent Deep Reinforcement Learning

Author : Matthew John Hausknecht
Publisher :
ISBN 13 :
Total Pages : 318 pages
Book Rating : 4.:/5 (972 download)

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Book Synopsis Cooperation and Communication in Multiagent Deep Reinforcement Learning by : Matthew John Hausknecht

Download or read book Cooperation and Communication in Multiagent Deep Reinforcement Learning written by Matthew John Hausknecht and published by . This book was released on 2017 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning is the area of machine learning concerned with learning which actions to execute in an unknown environment in order to maximize cumulative reward. As agents begin to perform tasks of genuine interest to humans, they will be faced with environments too complex for humans to predetermine the correct actions using hand-designed solutions. Instead, capable learning agents will be necessary to tackle complex real-world domains. However, traditional reinforcement learning algorithms have difficulty with domains featuring 1) high-dimensional continuous state spaces, for example pixels from a camera image, 2) high-dimensional parameterized-continuous action spaces, 3) partial observability, and 4) multiple independent learning agents. We hypothesize that deep neural networks hold the key to scaling reinforcement learning towards complex tasks. This thesis seeks to answer the following two-part question: 1) How can the power of Deep Neural Networks be leveraged to extend Reinforcement Learning to complex environments featuring partial observability, high-dimensional parameterized-continuous state and action spaces, and sparse rewards? 2) How can multiple Deep Reinforcement Learning agents learn to cooperate in a multiagent setting? To address the first part of this question, this thesis explores the idea of using recurrent neural networks to combat partial observability experienced by agents in the domain of Atari 2600 video games. Next, we design a deep reinforcement learning agent capable of discovering effective policies for the parameterized-continuous action space found in the Half Field Offense simulated soccer domain. To address the second part of this question, this thesis investigates architectures and algorithms suited for cooperative multiagent learning. We demonstrate that sharing parameters and memories between deep reinforcement learning agents fosters policy similarity, which can result in cooperative behavior. Additionally, we hypothesize that communication can further aid cooperation, and we present the Grounded Semantic Network (GSN), which learns a communication protocol grounded in the observation space and reward function of the task. In general, we find that the GSN is effective on domains featuring partial observability and asymmetric information. All in all, this thesis demonstrates that reinforcement learning combined with deep neural network function approximation can produce algorithms capable of discovering effective policies for domains with partial observability, parameterized-continuous actions spaces, and sparse rewards. Additionally, we demonstrate that single agent deep reinforcement learning algorithms can be naturally extended towards cooperative multiagent tasks featuring learned communication. These results represent a non-trivial step towards extending agent-based AI towards complex environments.

Adaptive and Learning Agents

Author : Peter Vrancx
Publisher : Springer Science & Business Media
ISBN 13 : 3642284981
Total Pages : 141 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Adaptive and Learning Agents by : Peter Vrancx

Download or read book Adaptive and Learning Agents written by Peter Vrancx and published by Springer Science & Business Media. This book was released on 2012-03-09 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Adaptive and Learning Agents, ALA 2011, held at the 10th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2011, in Taipei, Taiwan, in May 2011. The 7 revised full papers presented together with 1 invited talk were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on single and multi-agent reinforcement learning, supervised multiagent learning, adaptation and learning in dynamic environments, learning trust and reputation, minority games and agent coordination.

Layered Learning in Multi-Agent Systems

Author : Peter Stone
Publisher :
ISBN 13 :
Total Pages : 247 pages
Book Rating : 4.:/5 (227 download)

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Book Synopsis Layered Learning in Multi-Agent Systems by : Peter Stone

Download or read book Layered Learning in Multi-Agent Systems written by Peter Stone and published by . This book was released on 1998 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-agent systems in complex, real-time domains require agents to act effectively both autonomously and as part of a team. This dissertation addresses multi-agent systems consisting of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. Because of the inherent complexity of this type of multi-agent system, this thesis investigates the use of machine learning within multi-agent systems. The dissertation makes four main contributions to the fields of Machine Learning and Multi-Agent Systems. First, the thesis defines a team member agent architecture within which a flexible team structure is presented, allowing agents to decompose the task space into flexible roles and allowing them to smoothly switch roles while acting. Team organization is achieved by the introduction of a locker-room agreement as a collection of conventions followed by all team members. It defines agent roles, team formations, and pre-compiled multi-agent plans. In addition, the team member agent architecture includes a communication paradigm for domains with single-channel, low-bandwidth, unreliable communication. The communication paradigm facilitates team coordination while being robust to lost messages and active interference from opponents. Second, the thesis introduces layered learning, a general-purpose machine learning paradigm for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable. Given a hierarchical task decomposition, layered learning allows for learning at each level of the hierarchy, with learning at each level directly affecting learning at the next higher level. Third, the thesis introduces a new multi-agent reinforcement learning algorithm, namely team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL is designed for domains in which agents cannot necessarily observe the state changes when other team members act.

Addressing Multi-Objective and Domain Adaptation Challenges in Reinforcement Learning Through Case Studies in Multi-agent Navigation and Visual Servoing

Download Addressing Multi-Objective and Domain Adaptation Challenges in Reinforcement Learning Through Case Studies in Multi-agent Navigation and Visual Servoing PDF Online Free

Author : Salar Asayesh Ghalehseyf
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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Book Synopsis Addressing Multi-Objective and Domain Adaptation Challenges in Reinforcement Learning Through Case Studies in Multi-agent Navigation and Visual Servoing by : Salar Asayesh Ghalehseyf

Download or read book Addressing Multi-Objective and Domain Adaptation Challenges in Reinforcement Learning Through Case Studies in Multi-agent Navigation and Visual Servoing written by Salar Asayesh Ghalehseyf and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) has gained a lot of attention in recent years due to its potential to solve complex control problems. However, RL faces various challenges in multi-agent settings and high-dimensional action and observation spaces. This thesis addresses some of these challenges through two case studies: Multi-Agent Reinforcement Learning and Robotic Arm Manipulation. The first case study focuses on multi-agent navigation and proposes a novel class of RL-based controllers called least-restrictive controllers for multi-agent collision avoidance problems. This study aims to implement a high-level safe RL policy that provides safe navigation for multi-agent navigation in a shared environment with or without static obstacles. The proposed policy works in different tasks containing a different number of agents and different task objectives. The second case study proposes a novel visual servoing (VS) algorithm using sequential stochastic latent actor-critic and reinforcement learning. This study aims to overcome domain adaptation and control challenges in high-dimensional action and observation spaces. The proposed algorithm can adapt to a real robot through only single-shot transfer learning in representation learning parts.

Multi-Agent Reinforcement Learning

Author : Stefano V. Albrecht
Publisher : MIT Press
ISBN 13 : 0262380501
Total Pages : 0 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Multi-Agent Reinforcement Learning by : Stefano V. Albrecht

Download or read book Multi-Agent Reinforcement Learning written by Stefano V. Albrecht and published by MIT Press. This book was released on 2024-12-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches. Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the field’s foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers. First textbook to introduce the foundations and applications of MARL, written by experts in the field Integrates reinforcement learning, deep learning, and game theory Practical focus covers considerations for running experiments and describes environments for testing MARL algorithms Explains complex concepts in clear and simple language Classroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics Resources include code and slides

Multi-agent Reinforcement Learning Algorithms

Author : Natalia Akchurina
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Multi-agent Reinforcement Learning Algorithms by : Natalia Akchurina

Download or read book Multi-agent Reinforcement Learning Algorithms written by Natalia Akchurina and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Layered Learning in Multi-Agent Systems

Author : Peter Stone
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (48 download)

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Book Synopsis Layered Learning in Multi-Agent Systems by : Peter Stone

Download or read book Layered Learning in Multi-Agent Systems written by Peter Stone and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-agent systems in complex, real-time domains require agents to act effectively both autonomously and as part of a team. This dissertation addresses multi-agent systems consisting of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. Because of the inherent complexity of this type of multi-agent system, this thesis investigates the use of machine learning within multi-agent systems. The dissertation makes four main contributions to the fields of Machine Learning and Multi-Agent Systems. First, the thesis defines a team member agent architecture within which a flexible team structure is presented, allowing agents to decompose the task space into flexible roles and allowing them to smoothly switch roles while acting. Team organization is achieved by the introduction of a locker-room agreement as a collection of conventions followed by all team members. It defines agent roles, team formations, and pre-compiled multi-agent plans. In addition, the team member agent architecture includes a communication paradigm for domains with single-channel, low-bandwidth, unreliable communication. The communication paradigm facilitates team coordination while being robust to lost messages and active interference from opponents. Second, the thesis introduces layered learning, a general-purpose machine learning paradigm for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable. Given a hierarchical task decomposition, layered learning allows for learning at each level of the hierarchy, with learning at each level directly affecting learning at the next higher level. Third, the thesis introduces a new multi-agent reinforcement learning algorithm, namely team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL is designed for domains in which agents cannot necessarily observe the state changes when other team members act.

Multi-Agent Reinforcement Learning and Adaptive Neural Networks

Author :
Publisher :
ISBN 13 :
Total Pages : 22 pages
Book Rating : 4.:/5 (227 download)

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Book Synopsis Multi-Agent Reinforcement Learning and Adaptive Neural Networks by :

Download or read book Multi-Agent Reinforcement Learning and Adaptive Neural Networks written by and published by . This book was released on 1996 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project investigated learning systems consisting of multiple interacting controllers, or agents: each of which employed a modern reinforcement learning method. The objective was to study the utility of reinforcement learning as an approach to complex decentralized control problems. The major accomplishment was a detailed study of multi-agent reinforcement learning applied to a large-scale decentralized stochastic control problem. This study included a very successful demonstration that a multi-agent reinforcement learning system using neural networks could learn high-performance dispatching of multiple elevator cars in a simulated multi-story building. This problem is representative of very large-scale dynamic optimization problems of practical importance that are intractable for standard methods. The performance achieved by the distributed elevator controller surpassed that of the best of the elevator control algorithms accessible in the literature, showing that reinforcement learning can be a useful approach to difficult decentralized control problems. Additional empirical results demonstrated the performance of reinforcement learning-systems in the setting of nonzero-sum games, with mixed results. Some progress was also made in improving theoretical understanding of multi-agent reinforcement learning.

Handbook of Reinforcement Learning and Control

Author : Kyriakos G. Vamvoudakis
Publisher :
ISBN 13 : 9783030609917
Total Pages : 0 pages
Book Rating : 4.6/5 (99 download)

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Book Synopsis Handbook of Reinforcement Learning and Control by : Kyriakos G. Vamvoudakis

Download or read book Handbook of Reinforcement Learning and Control written by Kyriakos G. Vamvoudakis and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. 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. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative. .

Layered Learning in Multiagent Systems

Author : Peter Stone
Publisher : Bradford Books
ISBN 13 : 9780262194389
Total Pages : 272 pages
Book Rating : 4.1/5 (943 download)

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Book Synopsis Layered Learning in Multiagent Systems by : Peter Stone

Download or read book Layered Learning in Multiagent Systems written by Peter Stone and published by Bradford Books. This book was released on 2000 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. The book makes four main contributions to the fields of machine learning and multiagent systems. First, it describes an architecture within which a flexible team structure allows member agents to decompose a task into flexible roles and to switch roles while acting. Second, it presents layered learning, a general-purpose machine-learning method for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable with existing machine-learning methods. Third, the book introduces a new multiagent reinforcement learning algorithm--team-partitioned, opaque-transition reinforcement learning (TPOT-RL)--designed for domains in which agents cannot necessarily observe the state-changes caused by other agents' actions. The final contribution is a fully functioning multiagent system that incorporates learning in a real-time, noisy domain with teammates and adversaries--a computer-simulated robotic soccer team. Peter Stone's work is the basis for the CMUnited Robotic Soccer Team, which has dominated recent RoboCup competitions. RoboCup not only helps roboticists to prove their theories in a realistic situation, but has drawn considerable public and professional attention to the field of intelligent robotics. The CMUnited team won the 1999 Stockholm simulator competition, outscoring its opponents by the rather impressive cumulative score of 110-0.