Multi-agent Case-Based Reasoning for Cooperative Reinforcement Learners

Gabel, Thomas; Riedmiller, Martin

doi:10.1007/11805816_5

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…The authors developed a reduced-lists algorithm that still appears difficult to apply in large multi-state games. The use of function approximators with implicit coordination is also discussed in Gabel and Riedmiller (2006). Table 1 summarizes the characteristics of reviewed RL algorithms for ILs in cooperative games.…”

Section: Implicit Coordination With List Indexmentioning

confidence: 99%

Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems

Matignon

Laurent

Fort-Piat

2012

The Knowledge Engineering Review

359

266

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AbstractIn the framework of fully cooperative multi-agent systems, independent (non-communicative) agents that learn by reinforcement must overcome several difficulties to manage to coordinate. This paper identifies several challenges responsible for the non-coordination of independent agents: Pareto-selection, non-stationarity, stochasticity, alter-exploration and shadowed equilibria. A selection of multi-agent domains is classified according to those challenges: matrix games, Boutilier's coordination game, predators pursuit domains and a special multi-state game. Moreover, the performance of a range of algorithms for independent reinforcement learners is evaluated empirically. Those algorithms are Q-learning variants: decentralized Q-learning, distributed Q-learning, hysteretic Q-learning, recursive frequency maximum Q-value and win-or-learn fast policy hill climbing. An overview of the learning algorithms’ strengths and weaknesses against each challenge concludes the paper and can serve as a basis for choosing the appropriate algorithm for a new domain. Furthermore, the distilled challenges may assist in the design of new learning algorithms that overcome these problems and achieve higher performance in multi-agent applications.

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Section: Implicit Coordination With List Indexmentioning

confidence: 99%

Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems

Matignon

Laurent

Fort-Piat

2012

The Knowledge Engineering Review

359

266

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“…Case-based reasoning has been combined with value function approximation algorithms such as reinforcement learning [16,17,18,19] and neural networks [20,21]. The particular value function approximation algorithm developed in PITS++ has been shown to be particularly useful for spatial event prediction and, therefore, a suitable base line to measure performance gains by using case-based reasoning techniques.…”

Section: Related Workmentioning

confidence: 99%

Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning

Li¹,

Muñoz-Ávila

Bramsen³

et al. 2009

Case-Based Reasoning Research and Development

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Abstract. This paper presents a new approach for spatial event prediction that combines a value function approximation algorithm and case-based reasoning predictors. Each of these predictors makes unique contributions to the overall spatial event prediction. The function value approximation prediction is particularly suitable to reasoning with geographical features such as the (x,y) coordinates of an event. The case-based prediction is particularly well suited to deal with non-geographical features such as the time of the event or income level of the population. We claim that the combination of these two predictors results in a significant improvement of the accuracy in the spatial event prediction compared to pure geographically-based predictions. We support our claim by reporting on an ablation study for the prediction of improvised explosive device (IED) attacks.

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Reinforcement learning applications to machine scheduling problems: a comprehensive literature review

Kayhan

Yildiz

2021

J Intell Manuf

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Multi-agent Case-Based Reasoning for Cooperative Reinforcement Learners

Cited by 3 publications

References 12 publications

Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems

Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems

Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning

Reinforcement learning applications to machine scheduling problems: a comprehensive literature review

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