Abstract
Based on the idea of cooperative learning in multiagent systems, two approaches to design Mixed Performance Controllers (MPCs) are presented. Each controller is assumed as an expert agent. These agents share their knowledge to construct a MPC with mixture of initial controllers' properties. In the first approach, the knowledge of each agent is extracted and presented in a Q-table like tabular form. These tables are combined, and with greedy action selection policy the MPC is found. In the second approach, controllers are combined from a behavioural viewpoint. Stability of the MPC is formalised in a theorem with a sufficient condition. Simulation studies indicate that the combined controller shows a mixture of desired properties associated with individual controller agents.
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