Levels of Realism for Cooperative Multi-Agent Reinforcement Learning

Effect of levels of realism of mobile-based pedagogical agents on health e-learning

Background: One of the ways for effective communication between learners and instructional multimedia content in mobile learning systems is taking advantage of characters or pedagogical agents. The present study aimed to investigate the effect of the levels of realism in mobile-based pedagogical agents on health e-learning. Methods: The s...

Reinforcement Learning in Cooperative Multi–Agent Systems

Reinforcement Learning is used in cooperative multi–agent systems differently for various problems. We provide a review on learning algorithms used for repeated common–payoff games, and stochastic general– sum games. Then these learning algorithms is compared with another algorithm for the credit assignment problem that attempts to correctly assign agents the awards that they deserve.

Cooperative Multi-Agent Systems from the Reinforcement Learning Perspective - Challenges, Algorithms, and an Application

Reinforcement Learning has established as a framework that allows an autonomous agent for automatically acquiring – in a trial and error-based manner – a behavior policy based on a specification of the desired behavior of the system. In a multi-agent system, however, the decentralization of the control and observation of the system among independent agents has a significant impact on learning a...

An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems

Multi-agent Case-Based Reasoning for Cooperative Reinforcement Learners

In both research fields, Case-Based Reasoning and Reinforcement Learning, the system under consideration gains its expertise from experience. Utilizing this fundamental common ground as well as further characteristics and results of these two disciplines, in this paper we develop an approach that facilitates the distributed learning of behaviour policies in cooperative multi-agent domains witho...