This paper proposes an artificial intelligence network architecture based on software-defined network. The system has two independent characteristics of centralized control, distributed routing, control and forwarding...
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The Hydro-Environmental Autonomous Cleaning System (HEACS) addresses the critical issue of water pollution by autonomously collecting waste from water bodies and monitoring water quality. This system integrates a conv...
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This study aims to address the issues of low efficiency and high manual involvement in existing government affairs processes by proposing an automated system based on large model *** system integrates modules such as ...
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In recent years, with the rapid development of science and technology towards information and intelligence, unmanned war mode is gradually becoming the new trend of future war development. In the unmanned system archi...
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Automatic quality examination is now receiving attention as a vital element of the industry 4.o. However, because of the variety of products, challenges associated with creating uniformly high-quality product pictures...
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Delivering a good quality product is the prime requirement for software development process in which the quality aspects include consistency, reproducibility, environment dependency, etc. wherein execution time is a d...
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Human-computer interaction (HCI) is crucial to improving public transportation in low-resource settings. The degree that people give up control to computers is a topic of discussion in various settings, including impl...
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This paper introduces a weak optical signal processing scheme based on FPGA. Through the design of weak signal conditioning module, data acquisition control module, FPGA and peripheral modules, multi-channel acquisiti...
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This paper investigates a novel belt-type water surface garbage collector, designed to enhance the intelligence and efficiency of garbage collection systems. To address the mechanical structure and control system of t...
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This paper investigates the advantages and boundaries of actor-critic reinforcement learning algorithms in an industrial setting. We compare and discuss Cycle of Learning, Deep Deterministic Policy Gradient and Twin D...
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ISBN:
(数字)9781665490429
ISBN:
(纸本)9781665490429
This paper investigates the advantages and boundaries of actor-critic reinforcement learning algorithms in an industrial setting. We compare and discuss Cycle of Learning, Deep Deterministic Policy Gradient and Twin Delayed Deep Deterministic Policy Gradient with respect to performance in simulation as well as on a real robot setup. Furthermore, it emphasizes the importance and potential of combining demonstrated expert behavior with the actor-critic reinforcement learning setting while using it with an admittance controller to solve an industrial assembly task. Cycle of Learning and Twin Delayed Deep Deterministic Policy Gradient showed to be equally usable in simulation, while Cycle of Learning proved to be best on a real world application due to the behavior cloning loss that enables the agent to learn rapidly. The results also demonstrated that it is a necessity to incorporate an admittance controller in order to transfer the learned behavior to a real robot.
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