This tutorial introduces adaptive control, actor-critic reinforcement algorithms, and a new way to combine these two paradigms for data-driven decision making on a classical locomotion control problem.
Abstract
Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields have common roots in dynamic programming, they have evolved with distinct methodologies, goals, and cultures. Despite decades of mutual influence, a significant gap persists between the two communities. This tutorial introduces adaptive control, actor-critic reinforcement algorithms, and a new way to combine these two paradigms for data-driven decision making on a classical locomotion control problem. Our aim is to provide a foundation for understanding the core differences between the two approaches and insights to help experts in each field better understand and engage with the tools and approaches of the other.
This work states that there has been an upsurge of interest and development of RL theory and applications in the communities of stochastic control, stochastic analysis and unsupervised learning.
Xin Guo, Xun-Yu Zhou· Applied Mathematics and Opti...· 0 citations
The term"dual control"refers to the dual objective of simultaneously balancing exploration and exploitation. Problems of this kind have been studied for nearly a century. This paper is devoted to theory and methodology relevant for optimal control of linear time-invariant systems whose parameters are initially unknown...
The findings demonstrate MARL’s promise in solving navigation problems efficiently and provide concrete recommendations for tuning training parameters and network structures to enhance performance and robustness.
Stanislav Safranek, Brian M. Kirk· International Journal of Inn...· 0 citations
This work introduces Gradient Control Rewards (GCR), an interpretable, control-inspired reward-design methodology for accelerating agent training by modulating the reward signal based on the temporal dynamics of system error, Inspired by classical control theory.
Anas Aburaya, H. Selamat, M. Muslim et al.· IEEE Access· 0 citations
A survey examines where reinforcement learning algorithms currently stand within this field, and lays out the core theory and terminology involved, and highlights the principal obstacles that still restrict these algorithms from being deployed on real-world robotic problems.
Sachidananda M. H., P. R, D. R.· International Journal for Re...· 0 citations
Examples from UAV-assisted networks, SD-WAN traffic engineering, safe control, and reasoning-based AI connect mathematical concepts to practical challenges such as partial observability, competing objectives, safety constraints, deployment drift, and uncertain evaluation.
Ghoshana Bista· 0 citations
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