Evolutionary game theory and multi-agent reinforcement learning

Tuyls, Karl; Nowé, Ann

doi:10.1017/s026988890500041x

Cited by 97 publications

(59 citation statements)

References 39 publications

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“…Some of the pioneering works in the use of gamification techniques were those proposed by Tuyls et al [77,78] in which they used Reinforcement Learning (RL) to model the learning of the agents. These RL algorithms are sensitive to the correct choice of parameters so it is necessary to make a correct choice of parameter settings.…”

Section: Wsn Management Mas For Optimization Decision-makingmentioning

confidence: 99%

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

et al. 2018

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This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

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Section: Wsn Management Mas For Optimization Decision-makingmentioning

confidence: 99%

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

et al. 2018

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“…So far, game-theory-based analysis has only been applied to the learning dynamics [3], [37]. We expect that tools developed in the area of robust control will play an important role in the analysis and synthesis of the learning process as a whole (i.e., the environment and the learning dynamics).…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Multi-Agent Reinforcement Learning: A Survey

Buşoniu

Babuška

Schutter

2006

2006 9th International Conference on Control, Automation, Robotics and Vision

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Abstract-Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. However, due to different viewpoints on central issues, such as the formal statement of the learning goal, a large number of different methods and approaches have been introduced. In this paper we aim to present an integrated survey of the field. First, the issue of the multi-agent learning goal is discussed, after which a representative selection of algorithms is reviewed. Finally, open issues are identified and future research directions are outlined.

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“…It is shown in [12] that replicator dynamics based on evolutionary game theory and the learning automata [13] are quite similar and are actually equivalent in some circumstances. However, learning automata are computationally simple and efficient and thus are more appropriate in designing practical distributed algorithm with limited information.…”

Section: Introductionmentioning

confidence: 99%

Adaptive mechanism design and game theoretic analysis of auction-driven dynamic spectrum access in cognitive radio networks

Zhong

Wang

et al. 2014

J Wireless Com Network

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This paper studies the auction-driven dynamic spectrum access in cognitive radio networks with heterogeneous secondary users, who have different risk attitudes. First, a game theoretic framework is established for auction-driven dynamic spectrum access in cognitive radio networks. The utility functions and bidding strategies of heterogeneous secondary users are defined, and the parameterized auction mechanisms of primary user are also introduced. Then, we formulate the auction-driven dynamic spectrum access problem as a finite discrete game with a mixed-or pure-strategy Nash equilibrium solution. We study the existence and uniqueness properties of the pure-strategy Nash equilibrium in the defined game. Next, we propose a distributed learning automata algorithm (DLA) to attain the Nash equilibrium of the defined game with limited feedback. The adaptive mechanism design is realized in the updating procedure of our DLA algorithm. We further prove that our DLA algorithm converges to a Nash equilibrium of the defined game. Finally, simulation results show that our DLA algorithm is efficient and outperforms the dynamic spectrum access schemes with fixed auction mechanism.

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Evolutionary game theory and multi-agent reinforcement learning

Cited by 97 publications

References 39 publications

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Multi-Agent Reinforcement Learning: A Survey

Adaptive mechanism design and game theoretic analysis of auction-driven dynamic spectrum access in cognitive radio networks

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