… better generalization abilities in deep reinforcement learning (RL). … learning process. The
proposed method, which we term gradient monitoring (GM), is a method to steer the learning in …

… Reinforcement Learning-based Gradient Monitored (RLGM) mechanism. RLGM maintains
safe regions and reduces gradient … provides a better accuracy of the learning goal. In addition, …

… We propose different stream-based, adaptive learning approaches to analyze network-…
-based machine learning and online network monitoring: ( i ) how to dynamically learn from and …

… the gradient ascent method to maximize function J ( θ ) . Policy gradient methods are limited
by gradient … A subspace of the full state–space of the structure’s response is monitored by a …

… Mansour, “Policy gradient methods for reinforcement learning with function approximation,”
in Proceedings of the 12th International Conference on Neural Information Processing …

… air conditioning (HVAC) system and monitored for performance degradation by tracking an
… gradient based reinforcement learning algorithm with some augmentations as the learning …

… control lab setup using the Deep Deterministic Policy Gradient (DDPG) algorithm. The work
… Monitoring and maintaining the performance over time is typically costly and complicated. …

This research focuses on real-time monitoring and analysis of track and field athletes,
addressing the limitations of traditional monitoring systems in terms of real-time performance and …

… , relying on a Deep Reinforcement Learning algorithm, is … A Policy Gradients RL is used
to decide on whether to … through the use of Deep Reinforcement Learning, on the base of …

… policy gradient algorithm(DDPG) compensation. The running state of BLDCM is monitored
by … Depth deterministic strategy gradient algorithm is a deep reinforcement learning algorithm …