When applying reinforcement learning algorithms such as Q-learning to real world problems, we must consider the high and redundant dimensions and continuity of the state-action space. The continuity of state-action sp...
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(纸本)9781479959556
When applying reinforcement learning algorithms such as Q-learning to real world problems, we must consider the high and redundant dimensions and continuity of the state-action space. The continuity of state-action space is often treated by value function approximation. However, conventional function approximators such as radial basis function networks (RBFNs) are unsuitable in these environments, because they incur high computational cost, and the number of required experiences grows exponentially with the dimension of the state-action space. By contrast, selective desensitization neural network (SDNN) is highly robust to redundant inputs and computes at low computational cost. This paper proposes a novel function approximation method for Q-learning in continuous state-action space based on SDNN. The proposed method is evaluated by numerical experiments with redundant input(s). These experimental results validate the robustness of the proposed method to redundant state dimensions, and its lower computational cost than RBFN. These properties are advantageous to real-world applications such as robotic systems.
To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with...
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To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short and medium term simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.
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