Search Results - (( program navigation learning algorithm ) OR ( java application drops algorithm ))
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Collision prediction based genetic network programming-reinforcement learning for mobile robot navigation in unknown dynamic environments
Published 2017“…The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. …”
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Deep Reinforcement Learning For Control
Published 2021“…As a consequence, the agent is expected to have trained behaviors and navigation without crashing. The complete project is carried out in the CARLA simulator to determine how to operate in discrete action space using Deep Reinforcement Learning (DRL) algorithms. …”
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Monograph -
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Optimized processing of satellite signal via evolutionary search algorithm
Published 2000“…Researchers from the Satellite Navigation Research Group (SNAG) of UTM are currently conducting a research program that mitigates the effect of the Anti-Spoofing (AS) policy. …”
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LinguaBridge – academic digital scaffold
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Conference or Workshop Item -
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Designing machine learning frameworks for intelligence and gamification research / Nordin Abu Bakar
Published 2016“…Machine learning frameworks have been utilised to facilitate intelligence as operational mechanism in intelligence embedded system such as learning system, prediction protocol and robot navigation system. …”
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Thesis -
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Robotics in Education
Published 2026“…The AI section discusses machine learning, path-planning algorithms (e.g., A* search, SLAM), and classroom case studies. …”
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Double Deep RL-based strategy for UAV-assisted energy harvesting optimization in disaster-resilient IoT networks
Published 2024“…Due to the problem's complexity, we propose a lightweight DDRL solution capable of efficiently learning system dynamics. Extensive simulations and comparisons with Deep RL and DDPG algorithms demonstrate the superior performance of DDRL in enhancing EH, covering strategic locations effectively, and achieving high satisfaction and accuracy rates.…”
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