Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Mascem: A Multiagent System That Simulates Competitive Electricity Markets
IEEE Intelligent Systems
Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Handbook of Computational Economics)
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Multiagent reinforcement learning using function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A novel approach to multiagent reinforcement learning: utilizing OLAP mining in the learning process
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Software agents and market (in) efficiency: a human trader experiment
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Agent-based modeling of supply chains for distributed scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Autonomous data-driven decision-making in smart electricity markets
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Backward Q-learning: The combination of Sarsa algorithm and Q-learning
Engineering Applications of Artificial Intelligence
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Balancing between exploration and exploitation with adaptation of the Q-learning (QL) parameters to the condition of dynamic uncertain environment has always been a significant subject of interest in the context of reinforcement learning. The peculiarities of the electricity market have provided such complex dynamic economic environment, and consequently have increased the requirement for advancement of the learning methods. In this economic system, the agent's market power plays a vital role in bidding decision-making problem. In order to improve the QL method, as main idea, adaptation of its parameters to the market power is proposed for making a good balance between exploration and exploitation. To implement this adaptation process, due to the fuzzy nature of human's decision-making process, a fuzzy system is designed to map each agent's market power into the QL parameters. Therefore, a fuzzy QL method is developed to model the power supplier's strategic bidding behavior in a computational electricity market. In the simulation framework, the QL algorithm selects the power supplier's bidding strategy according to the past experiences and the values of the parameters, which show the human's risk characteristic. The application of the proposed methodology for the power supplier in a multiarea power system shows the performance improvement in comparison to the QL with fixed parameters.