Please use this identifier to cite or link to this item: https://doi.org/10.1109/ADPRL.2013.6614997
Title: A reinforcement learning algorithm developed to model GenCo strategic bidding behavior in multidimensional and continuous state and action spaces
Authors: Lau, A.Y.F.
Srinivasan, D. 
Reindl, T.
Keywords: agent-based modeling
electricity market
reinforcement learning
strategic bidding behavior
Issue Date: 2013
Source: Lau, A.Y.F.,Srinivasan, D.,Reindl, T. (2013). A reinforcement learning algorithm developed to model GenCo strategic bidding behavior in multidimensional and continuous state and action spaces. IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL : 116-123. ScholarBank@NUS Repository. https://doi.org/10.1109/ADPRL.2013.6614997
Abstract: The electricity market have provided a complex economic environment, and consequently have increased the requirement for advancement of learning methods. In the agent-based modeling and simulation framework of this economic system, the generation company's decision-making is modeled using reinforcement learning. Existing learning methods that models the generation company's strategic bidding behavior are not adapted to the non-stationary and non-Markovian environment involving multidimensional and continuous state and action spaces. This paper proposes a reinforcement learning method to overcome these limitations. The proposed method discovers the input space structure through the self-organizing map, exploits learned experience through Roth-Erev reinforcement learning and the explores through the actor critic map. Simulation results from experiments show that the proposed method outperforms Simulated Annealing Q-Learning and Variant Roth-Erev reinforcement learning. The proposed method is a step towards more realistic agent learning in Agent-based Computational Economics. © 2013 IEEE.
Source Title: IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL
URI: http://scholarbank.nus.edu.sg/handle/10635/83413
ISBN: 9781467359252
ISSN: 23251824
DOI: 10.1109/ADPRL.2013.6614997
Appears in Collections:Staff Publications

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