Contextual Q-Learning

Vale, Zita; Pinto, Tiago

http://hdl.handle.net/10400.22/17298

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Autores

Vale, Zita

Pinto, Tiago

Resumo(s)

This paper highlights a new learning model that introduces a contextual dimension to the well-known Q-Learning algorithm. Through the identification of different contexts, the learning process is adapted accordingly, thus converging to enhanced results. The proposed learning model includes a simulated annealing (SA) process that accelerates the convergence process. The model is integrated in a multi-agent decision support system for electricity market players negotiations, enabling the experimentation of results using real electricity market data.