An extension of a hierarchical reinforcement learning algorithm for multiagent settings

Authors:
Ioannis Lambrou;Vassilis Vassiliades;Chris Christodoulou
Affiliations:
Department of Computer Science, University of Cyprus, Nicosia, Cyprus;Department of Computer Science, University of Cyprus, Nicosia, Cyprus;Department of Computer Science, University of Cyprus, Nicosia, Cyprus
Venue:
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Year:
2011

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Abstract

This paper compares and investigates single-agent reinforcement learning (RL) algorithms on the simple and an extended taxi problem domain, and multiagent RL algorithms on a multiagent extension of the simple taxi problem domain we created. In particular, we extend the Policy Hill Climbing (PHC) and the Win or Learn Fast-PHC (WoLF-PHC) algorithms by combining them with the MAXQ hierarchical decomposition and investigate their efficiency. The results are very promising for the multiagent domain as they indicate that these two newly-created algorithms are the most efficient ones from the algorithms we compared.