A major drawback of reinforcement learning (RL) is the slow learning rate. We are interested in speeding up RL. We first approached this problem with transfer learning where we have two domains. We developed a method to transfer knowledge from a completely trained RL domain to a partially trained related domain (where we want to speed up learning) and this helped increase the learning rate suff...

Reinforcement learning is a machine intelligence scheme for learning in highly dynamic and probabilistic environments. The methodology, however, suffers from a major drawback; the convergence to an optimal solution usually requires high computational expense since all states should be visited frequently in order to guarantee a reliable policy. In this paper, a new reinforcement learning algorit...

Reinforcement learning has received much attention in the past decade. The primary thrust of this research has focused on tabula rasa learning methods. That is, the learning agent is initially unaware of its environment and must learn or re-learn everything. We feel that this is neither realistic nor effective. While the agent may start out with little or no knowledge of its environment, it mus...

Historically, transmission systems are built together with power production installations in order to meet the expected electricity consumption. For economic reasons, they are usually not overdimensioned and therefore cannot guarantee power transmission capacity for new power plants for 100% of the year. Wind power plants have to be installed in the immediate proximity of the resource – wind. T...

Smart Grid markets are dynamic and complex, and brokers are widely introduced to better manage the markets. However, brokers face great challenges, including the varying energy demands of consumers, the changing prices in the markets, and the competitions between each other. This paper proposes an intelligent broker model based on hybrid learning (including unsupervised, supervised and reinforc...

The smart grid concept is key to the energy revolution that has been taking place in recent years. Smart Grids have present research since their emergence. However, scarcity of data from different sources, hardware power, or co-simulation environments hindered development. With advances multi-agent-based systems, possibility simulating behavior combining real building consumption, and simulated...

Dynamic Programming, Q-Iearning and other discrete Markov Decision Process solvers can be -applied to continuous d-dimensional state-spaces by quantizing the state space into an array of boxes. This is often problematic above two dimensions: a coarse quantization can lead to poor policies, and fine quantization is too expensive. Possible solutions are variable-resolution discretization, or func...

A finite difference based Stokes solver is developed to predict the mesoscopic permeability of textile reinforcements. In general, a high grid resolution is employed (either globally or locally) to capture the geometrical details of the reinforcement. Local grid refinements often result in poor aspect ratios of the elements, resulting in inaccurate results, whereas the computational cost of com...