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Full-Text Articles in Engineering
Reinforcement Learning Approach For Inspect/Correct Tasks, Hoda Nasereddin
Reinforcement Learning Approach For Inspect/Correct Tasks, Hoda Nasereddin
LSU Doctoral Dissertations
In this research, we focus on the application of reinforcement learning (RL) in automated agent tasks involving considerable target variability (i.e., characterized by stochastic distributions); in particular, learning of inspect/correct tasks. Examples include automated identification & correction of rivet failures in airplane maintenance procedures, and automated cleaning of surgical instruments in a hospital sterilization processing department. The location of defects and the corrective action to be taken for each varies from task episode. What needs to be learned are optimal stochastic strategies rather than optimization of any one single defect type and location. RL has been widely applied in robotics …
Reinforcement Learning Based Maximum Power Point Tracking Control Of Partially Shaded Photovoltaic System, Kuan-Yu Chou, Chia-Shiou Yang, Yon-Ping Chen
Reinforcement Learning Based Maximum Power Point Tracking Control Of Partially Shaded Photovoltaic System, Kuan-Yu Chou, Chia-Shiou Yang, Yon-Ping Chen
Journal of Marine Science and Technology
Under the sun insolation in the daytime, the Maximum Power Point Tracking (MPPT) technique is usually used to achieve the maximum power in the photovoltaic (PV) system and often implemented by the Perturbation and Observation (P&O) method. However, due to the use of fixed step size, the P&O method will generate undesired oscillation around the maximum power point (MPP) and thus reduce the tracking efficiency. Besides, the output power of PV modules highly depends on the environment factors such as irradiance and temperature, especially for a PV array, which is formed by PV modules connected in series and parallel. The …
Formal Language Constraints In Deep Reinforcement Learning For Self-Driving Vehicles, Tyler Bienhoff
Formal Language Constraints In Deep Reinforcement Learning For Self-Driving Vehicles, Tyler Bienhoff
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
In recent years, self-driving vehicles have become a holy grail technology that, once fully developed, could radically change the daily behaviors of people and enhance safety. The complexities of controlling a car in a constantly changing environment are too immense to directly program how the vehicle should behave in each specific scenario. Thus, a common technique when developing autonomous vehicles is to use reinforcement learning, where vehicles can be trained in simulated and real-world environments to make proper decisions in a wide variety of scenarios. Reinforcement learning models, however, have uncertainties in how the vehicle acts, especially in a previously …
A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan
A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan
Open Access Theses & Dissertations
Soft robotics is a growing field in robotics research. Heavily inspired by biological systems, these robots are made of softer, non-linear, materials such as elastomers and are actuated using several novel methods, from fluidic actuation channels to shape changing materials such as electro-active polymers. Highly non-linear materials make modeling difficult, and sensors are still an area of active research. These issues have rendered typical control and modeling techniques often inadequate for soft robotics. Reinforcement learning is a branch of machine learning that focuses on model-less control by mapping states to actions that maximize a specific reward signal. Reinforcement learning has …