Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Putting more brain-like intelligence into the electric power grid: what we need and how to do it
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge
INFORMS Journal on Computing
Algorithms for Reinforcement Learning
Algorithms for Reinforcement Learning
Reinforcement Learning and Dynamic Programming Using Function Approximators
Reinforcement Learning and Dynamic Programming Using Function Approximators
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Research Commentary---Designing Smart Markets
Information Systems Research
Strategy learning for autonomous agents in smart grid markets
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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For the vision of a Smart Grid to materialize, substantial advances in intelligent decentralized control mechanisms are required. We propose a novel class of autonomous broker agents for retail electricity trading that can operate in a wide range of Smart Electricity Markets, and that are capable of deriving long-term, profit-maximizing policies. Our brokers use Reinforcement Learning with function approximation, they can accommodate arbitrary economic signals from their environments, and they learn efficiently over the large state spaces resulting from these signals. Our design is the first that can accommodate an offline training phase so as to automatically optimize the broker for particular market conditions. We demonstrate the performance of our design in a series of experiments using real-world energy market data, and find that it outperforms previous approaches by a significant margin.