The field of autonomous systems and reinforcement learning is rapidly evolving, with a focus on developing more efficient, adaptive, and reliable control methods. Recent research has explored the use of hierarchical reinforcement learning, Lyapunov function-guided control, and multi-timescale stability-preserving frameworks to improve the performance and stability of autonomous systems. Additionally, there has been a growing interest in applying reinforcement learning to real-world problems, such as robotic arm control, autonomous navigation, and space exploration. Noteworthy papers in this area include: A Novel Multi-Timescale Stability-Preserving Hierarchical Reinforcement Learning Controller Framework, which introduces a new framework for controlling high-dimensional stochastic systems. RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets, which presents a reinforcement learning framework for autonomous visual inspection of space targets. Transferable Deep Reinforcement Learning for Cross-Domain Navigation, which investigates the feasibility of transferring reinforcement learning policies across different environments.
Advancements in Autonomous Systems and Reinforcement Learning
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Lights-Out: An Automated Ground Segment for unstaffed Satellite Operations
Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
A Novel Multi-Timescale Stability-Preserving Hierarchical Reinforcement Learning Controller Framework for Adaptive Control in High-Dimensional Dynamical Systems
RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets
Lyapunov Function-guided Reinforcement Learning for Flight Control
Never Too Rigid to Reach: Adaptive Virtual Model Control with LLM- and Lyapunov-Based Reinforcement Learning
TARC: Time-Adaptive Robotic Control
Transferable Deep Reinforcement Learning for Cross-Domain Navigation: from Farmland to the Moon
From Embedding to Control: Representations for Stochastic Multi-Object Systems
Hybrid DQN-TD3 Reinforcement Learning for Autonomous Navigation in Dynamic Environments