156,264 research outputs found
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
Within the context of autonomous driving a model-based reinforcement learning
algorithm is proposed for the design of neural network-parameterized
controllers. Classical model-based control methods, which include sampling- and
lattice-based algorithms and model predictive control, suffer from the
trade-off between model complexity and computational burden required for the
online solution of expensive optimization or search problems at every short
sampling time. To circumvent this trade-off, a 2-step procedure is motivated:
first learning of a controller during offline training based on an arbitrarily
complicated mathematical system model, before online fast feedforward
evaluation of the trained controller. The contribution of this paper is the
proposition of a simple gradient-free and model-based algorithm for deep
reinforcement learning using task separation with hill climbing (TSHC). In
particular, (i) simultaneous training on separate deterministic tasks with the
purpose of encoding many motion primitives in a neural network, and (ii) the
employment of maximally sparse rewards in combination with virtual velocity
constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl
Neural networks for small scale ORC optimization
This study concerns a thermodynamic and technical optimization of a small scale Organic Rankine Cycle system for waste heat
recovery applications. An Artificial Neural Network (ANN) has been used to develop a thermodynamic model to be used for
the maximization of the production of power while keeping the size of the heat exchangers and hence the cost of the plant at its
minimum. R1234yf has been selected as the working fluid. The results show that the use of ANN is promising in solving complex
nonlinear optimization problems that arise in the field of thermodynamics
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