Essays about: "policy gradient"
Showing result 16 - 20 of 44 essays containing the words policy gradient.
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16. Data Driven Energy Efficiency of Ships
University essay from Högskolan i Halmstad/Akademin för informationsteknologiAbstract : Decreasing the fuel consumption and thus greenhouse gas emissions of vessels has emerged as a critical topic for both ship operators and policy makers in recent years. The speed of vessels has long been recognized to have highest impact on fuel consumption. READ MORE
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17. Spiking Reinforcement Learning for Robust Robot Control Under Varying Operating Conditions
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Over the last few years, deep reinforcement learning (RL) has gained increasing popularity for its successful application to a variety of complex control and decision-making tasks. As the demand for deep RL algorithms deployed in challenging real-world environments grows, their robustness towards uncertainty, disturbances and perturbations of the environment becomes more and more important. READ MORE
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18. Deep Reinforcement Learning for Dynamic Grasping
University essay from Uppsala universitet/Avdelningen för systemteknikAbstract : Dynamic grasping is the action of, using only contact force, manipulating the position of a moving object in space. Doing so with a robot is a quite complex task in itself, but is one with wide-ranging applications. READ MORE
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19. Deep Reinforcement Learning for Building Control : A comparative study for applying Deep Reinforcement Learning to Building Energy Management
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Energy and environment have become hot topics in the world. The building sector accounts for a high proportion of energy consumption, with over one-third of energy use globally. A variety of optimization methods have been proposed for building energy management, which are mainly divided into two types: model-based and model-free. READ MORE
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20. Reinforcement learning for train dispatching : A study on the possibility to use reinforcement learning to optimize train ordering and minimize train delays in disrupted situations, inside the r ail simulator OSRD
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Train dispatching is a complex process, especially when the train traffic is disrupted, as the decisions taken by the dispatchers can have substantial consequences on the delays of the trains. The most frequent dispatching decisions consists in changing the order of trains at convergence points, where two tracks unite to become a single track. READ MORE
