Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions

This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case-Based Reasoning (CBR) and Multiagent Reinforcement Learning (MRL) techniques. This approach, called Case-Based Heuristically Accelerated Multiagent Reinforcement Learning (CB-HAMRL), builds upon an emerging technique, Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HAMRL is a subset of MRL that makes use of a heuristic function H derived from a case base, in a Case-Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Minimax

• Type: Conference
• Year: 2010
• Where: ECAI
• Authors: Reinaldo A. C. Bianchi, Ramon López de Mántaras
• Keywords: Artificial Intelligence, Case-based Reasoning, ECAI 2010, Multiagent Reinforcement, Reinforcement Learning