RL-Storage applies deep Q-learning to storage parameter tuning and claims up to 2.6x throughput gains and 43% latency reduction, but the evidence is not rigorously presented.
TD3 Based Collision Free Motion Planning for Robot Navigation
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abstract
This paper addresses the challenge of collision-free motion planning in automated navigation within complex environments. Utilizing advancements in Deep Reinforcement Learning (DRL) and sensor technologies like LiDAR, we propose the TD3-DWA algorithm, an innovative fusion of the traditional Dynamic Window Approach (DWA) with the Twin Delayed Deep Deterministic Policy Gradient (TD3). This hybrid algorithm enhances the efficiency of robotic path planning by optimizing the sampling interval parameters of DWA to effectively navigate around both static and dynamic obstacles. The performance of the TD3-DWA algorithm is validated through various simulation experiments, demonstrating its potential to significantly improve the reliability and safety of autonomous navigation systems.
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Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques
RL-Storage applies deep Q-learning to storage parameter tuning and claims up to 2.6x throughput gains and 43% latency reduction, but the evidence is not rigorously presented.