advice-exchange in heterogeneous groups of learning agents
This research aims at studying the effects of exchanging information during the learning process in Multiagent Systems . The concept of advice-exchange , introduced in [3], consists in requesting extra feedback, in the form of episodic advice, from other agents that are solving similar problems. This work is concerned with the exchange of information in heterogeneous groups of learning-agents that either share the same environment or are solving problems with similar structure. Concepts, such as self confidence , trust and advisor preference , were introduced in the experiments that led to the results discussed in this paper.
advice-exchange multiagent systems machine learning