Reinforcement learning techniques for optimal power control in grid-connected microgrids: A comprehensive review
Utility grids are undergoing several upgrades. Distributed generators that are supplied by
intermittent renewable energy sources (RES) are being connected to the grids. As RES get …
intermittent renewable energy sources (RES) are being connected to the grids. As RES get …
Deep reinforcement learning-based air combat maneuver decision-making: literature review, implementation tutorial and future direction
Nowadays, various innovative air combat paradigms that rely on unmanned aerial vehicles
(UAVs), ie, UAV swarm and UAV-manned aircraft cooperation, have received great attention …
(UAVs), ie, UAV swarm and UAV-manned aircraft cooperation, have received great attention …
Strategies for controlling microgrid networks with energy storage systems: A review
Distributed Energy Storage Systems are considered key enablers in the transition from the
traditional centralized power system to a smarter, autonomous, and decentralized system …
traditional centralized power system to a smarter, autonomous, and decentralized system …
Self-contrastive Learning-optimized General Agent for long-tailed fault diagnosis of shipboard antennas leveraging adaptive data distribution
Q Cui, S He, C Hu, J Bao, Y Peng, J Chen - Measurement, 2025 - Elsevier
To address the challenges of low accuracy and limited generalization in long-tailed fault
diagnosis, an adaptive data distribution-based reinforcement learning General Agent is …
diagnosis, an adaptive data distribution-based reinforcement learning General Agent is …
Mobile Robot Navigation Based on Noisy N-Step Dueling Double Deep Q-Network and Prioritized Experience Replay
Effective real-time autonomous navigation for mobile robots in static and dynamic
environments has become a challenging and active research topic. Although the …
environments has become a challenging and active research topic. Although the …
[HTML][HTML] Improved exploration–exploitation trade-off through adaptive prioritized experience replay
Experience replay is an indispensable part of deep reinforcement learning algorithms that
enables the agent to revisit and reuse its past and recent experiences to update the network …
enables the agent to revisit and reuse its past and recent experiences to update the network …
Prioritized Generative Replay
Sample-efficient online reinforcement learning often uses replay buffers to store experience
for reuse when updating the value function. However, uniform replay is inefficient, since …
for reuse when updating the value function. However, uniform replay is inefficient, since …
Prioritized experience replay based on multi-armed bandit
Experience replay has been widely used in deep reinforcement learning. The learning
algorithm allows online reinforcement learning agents to remember and reuse experiences …
algorithm allows online reinforcement learning agents to remember and reuse experiences …
Self-adaptive priority correction for prioritized experience replay
H Zhang, C Qu, J Zhang, J Li - Applied sciences, 2020 - mdpi.com
Deep Reinforcement Learning (DRL) is a promising approach for general artificial
intelligence. However, most DRL methods suffer from the problem of data inefficiency. To …
intelligence. However, most DRL methods suffer from the problem of data inefficiency. To …
Deep reinforcement learning-based service-oriented resource allocation in smart grids
L **, Y Wang, Y Wang, Z Wang, X Wang, Y Chen - IEEE Access, 2021 - ieeexplore.ieee.org
Resource allocation has a direct and profound impact on the performance of resource-
limited smart grids with diversified services that need to be timely processed. In this paper …
limited smart grids with diversified services that need to be timely processed. In this paper …