TacTex'13: A Champion Adaptive Power Trading Agent
By Daniel Urieli, Peter Stone. 2014
68 citations recorded by the source corpus.
Sustainable energy systems of the future will no longer be able to rely on the current paradigm that energy supply follows demand. Many of the renewable energy resources do not produce power on demand, and therefore there is a need for new market structures that motivate sustainable behaviors by participants. The Power Trading Agent Competition (Power TAC) is a new annual competition that focuses on the design and operation of future retail power markets, specifically in smart grid environments with renewable energy production, smart metering, and autonomous agents acting on behalf of customers and retailers. It uses a rich, open-source simulation platform that is based on real-world data and state-of-the-art customer models. Its purpose is to help researchers understand the dynamics of customer and retailer decision-making, as well as the robustness of proposed market designs. This paper introduces TacTex'13, the champion agent from the inaugural competition in 2013. TacTex'13 learns and adapts to the environment in which it operates, by heavily relying on reinforcement learning and prediction methods. This paper describes the constituent components of TacTex'13 and examines its success through analysis of competition results and subsequent controlled experiments.
Subfields: Automotive Engineering, Electrical and Electronic Engineering, Management Science and Operations Research
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In Automotive Engineering
In Electrical and Electronic Engineering
- David Silver
- Pieter Abbeel
- Marc G. Bellemare
- Sergey Levine
- Balaraman Ravindran
- R´emi Munos
- Mohammad Norouzi
- Timothy P. Lillicrap
In Management Science and Operations Research
Citations in the corpus
- Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016, background): “Most of these real-world games involve decision making with imperfect information and high-dimensional information state spaces.”