An extension of a hierarchical reinforcement learning algorithm for multiagent settings
Christodoulou, Chris C.
Source9th European Workshop on Reinforcement Learning, EWRL 2011
Google Scholar check
MetadataShow full item record
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. © 2012 Springer-Verlag.
Showing items related by title, author, creator and subject.
Spiking neural networks with different reinforcement learning (RL) schemes in a multiagent setting Christodoulou, Chris C.; Cleanthous, A. (2010)This paper investigates the effectiveness of spiking agents when trained with reinforcement learning (RL) in a challenging multiagent task. In particular, it explores learning through rewardmodulated spike-timing dependent ...
Panayiotou, Tania; Chatzis, Sotirios P.; Panayiotou, Christos; Ellinas, Georgios (2018)This work examines a cost optimization problem for plug-in hybrid electric vehicles (PHEVs) used for service delivery, in the presence of energy consumption uncertainty. For the cost optimization problem, an optimal policy ...
Multiagent reinforcement learning: Spiking and nonspiking agents in the Iterated Prisoner's Dilemma Vassiliades, Vassilis; Cleanthous, A.; Christodoulou, Chris C. (2011)This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory ...