Multiagent reinforcement learning in extensive form games with complete information

The adoption of the Fifth Generation (5G) and beyond 5G networks is driving the demand for learning approaches that enable users to co-exist harmoniously in a multi-user distributed environment. Although resourceconstrained, the Cognitive Radio (CR) has been identified as a key enabler of distributed 5G and beyond networks due to its cognitive abilities and ability to access idle spectrum opportunistically. Reinforcement learning is well suited to meet the demand for learning in 5G and beyond 5G networks because it does not require the learning agent to have prior information about the environment in which it operates. Intuitively, CRs should be enabled to implement reinforcement learning to efficiently gain opportunistic access to spectrum and co-exist with each other. However, the application of reinforcement learning is straightforward in a single-agent environment and complex and resource intensive in a multi-agent and multi-objective learning environment. In this paper, (1) we present a brief history and overview of reinforcement learning and its limitations; (2) we provide a review of recent multi-agent learning methods proposed and multi-agent learning algorithms applied in Cognitive Radio (CR) networks; and (3) we further present a novel framework for multi-CR reinforcement learning and conclude with a synopsis of future research directions and recommendations.

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“…Refs. [35][36][37] and include variations of Q-learning and Nash Q-learning approaches. The extensive form games are described next.…”

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confidence: 99%

Towards a multi-agent reinforcement learning approach for joint sensing and sharing in cognitive radio networks

Rapetswa

Cheng

2023

Intell. and Converged Netw.

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“…We introduced EMG, which can be regarded as an extension to Markov games in which each game state is in extensive form with perfect information [17].…”

Section: Definitionmentioning

confidence: 99%

“…In this paper, we develop traditional PS based on game theoretic solvers, subgame perfect equilibrium points (SPE), for n-person general-sum multiagent systems with sequential action selection based on our previous papers [17], [24], [33]. The existing learning process, called EMG, is considered as a set of successive extensive form games with perfect information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-Based Reinforcement Learning in Multiagent Systems with Sequential Action Selection

Akramizadeh

Afshar

Menhaj

et al. 2011

IEICE Trans. Inf. & Syst.

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SUMMARYModel-based reinforcement learning uses the gathered information, during each experience, more efficiently than model-free reinforcement learning. This is especially interesting in multiagent systems, since a large number of experiences are necessary to achieve a good performance. In this paper, model-based reinforcement learning is developed for a group of self-interested agents with sequential action selection based on traditional prioritized sweeping. Every single situation of decision making in this learning process, called extensive Markov game, is modeled as n-person general-sum extensive form game with perfect information. A modified version of backward induction is proposed for action selection, which adjusts the tradeoff between selecting subgame perfect equilibrium points, as the optimal joint actions, and learning new joint actions. The algorithm is proved to be convergent and discussed based on the new results on the convergence of the traditional prioritized sweeping. key words: multiagent systems, Markov games, model-based reinforcement learning, extensive form game

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Improved Virtual Leader Based Formation Control for Nonholonomic Multi-UUV

Hao

Kang

et al. 2012

Communications in Computer and Information Science

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Multiagent reinforcement learning in extensive form games with complete information

Cited by 7 publications

References 5 publications

Towards a multi-agent reinforcement learning approach for joint sensing and sharing in cognitive radio networks

Towards a multi-agent reinforcement learning approach for joint sensing and sharing in cognitive radio networks

Model-Based Reinforcement Learning in Multiagent Systems with Sequential Action Selection

Improved Virtual Leader Based Formation Control for Nonholonomic Multi-UUV

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