Essays about: "Deep reinforcement learning"
Showing result 16 - 20 of 201 essays containing the words Deep reinforcement learning.
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16. Real-time adaptation of robotic knees using reinforcement control
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Microprocessor-controlled knees (MPK’s) allow amputees to walk with increasing ease and safety as technology progresses. As an amputee is fitted with a new MPK, the knee’s internal parameters are tuned to the user’s preferred settings in a controlled environment. READ MORE
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17. An efficient deep reinforcement learning approach to the energy management for a parallel hybrid electric vehicle
University essay from KTH/Skolan för industriell teknik och management (ITM)Abstract : In contemporary world, the global energy crisis and raise of greenhouse gas concentration in atmosphere necessitate the energy conservation and emission reduction. Hybrid electric vehicles (HEVs) can achieve great promise in reducing fuel consumption and greenhouse gas emissions by appropriate energy management strategies (EMSs). READ MORE
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18. Stabilizing Side Effects of Experience Replay With Different Network Sizes for Deep Q-Network
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This report investigates the effects of two different types of batch selection used for traininga Deep Reinforcement Learning agent in games. More specifically, the impact of thedifferent methods were tested for different sizes of Deep Neural Networks while using theDeep Q-Network (DQN) algorithm. READ MORE
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19. Optimizing Energy Consumption in a Real-Time System Using Artificial Intelligence
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : In energy-efficient real-time embedded system design, the objective is to reduce energy consumption while meeting the tasks' timing requirements. Real-time Dynamic Voltage and Frequency Scaling (DVFS) methods aim at achieving this by scaling the frequency at which a single processor or multiple processors in the system operate, but they often assume that the tasks' deadlines are known and their arrival times are regular. READ MORE
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20. Machine Learning-Based Instruction Scheduling for a DSP Architecture Compiler : Instruction Scheduling using Deep Reinforcement Learning and Graph Convolutional Networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Instruction Scheduling is a back-end compiler optimisation technique that can provide significant performance gains. It refers to ordering instructions in a particular order to reduce latency for processors with instruction-level parallelism. READ MORE
