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Continuous-time reinforcement learning control: A review of theoretical results, insights on performance, and needs for new designs
This exposition discusses continuous-time reinforcement learning (CT-RL) for the control of
affine nonlinear systems. We review four seminal methods that are the centerpieces of the …
affine nonlinear systems. We review four seminal methods that are the centerpieces of the …
Generalized policy iteration using tensor approximation for hybrid control
Control of dynamic systems involving hybrid actions is a challenging task in robotics. To
address this, we present a novel algorithm called Generalized Policy Iteration using Tensor …
address this, we present a novel algorithm called Generalized Policy Iteration using Tensor …
Reinforcement twinning: From digital twins to model-based reinforcement learning
The concept of digital twins promises to revolutionize engineering by offering new avenues
for optimization, control, and predictive maintenance. We propose a novel framework for …
for optimization, control, and predictive maintenance. We propose a novel framework for …
Kernel-Based Optimal Control: An Infinitesimal Generator Approach
This paper presents a novel approach for optimal control of nonlinear stochastic systems
using infinitesimal generator learning within infinite-dimensional reproducing kernel Hilbert …
using infinitesimal generator learning within infinite-dimensional reproducing kernel Hilbert …
Continuous-time reinforcement learning: New design algorithms with theoretical insights and performance guarantees
Continuous-time reinforcement learning (CT-RL) methods hold great promise in real-world
applications. Adaptive dynamic programming (ADP)-based CT-RL algorithms, especially …
applications. Adaptive dynamic programming (ADP)-based CT-RL algorithms, especially …
Reinforcement learning control of hypersonic vehicles and performance evaluations
This work presents a new framework for model-based continuous-time reinforcement
learning (CT-RL) control of hypersonic vehicles (HSVs). The predominant classes of CT-RL …
learning (CT-RL) control of hypersonic vehicles (HSVs). The predominant classes of CT-RL …
Managing temporal resolution in continuous value estimation: a fundamental trade-off
A default assumption in reinforcement learning (RL) and optimal control is that observations
arrive at discrete time points on a fixed clock cycle. Yet, many applications involve …
arrive at discrete time points on a fixed clock cycle. Yet, many applications involve …
Mitigating the curse of horizon in Monte-Carlo returns
The standard framework in reinforcement learning (RL) dictates that an agent should use
every observation collected from interactions with the environment when updating its value …
every observation collected from interactions with the environment when updating its value …
Optimal Control of Fluid Restless Multi-armed Bandits: A Machine Learning Approach
We propose a machine learning approach to the optimal control of fluid restless multi-armed
bandits (FRMABs) with state equations that are either affine or quadratic in the state …
bandits (FRMABs) with state equations that are either affine or quadratic in the state …
A New, Physics-Informed Continuous-Time Reinforcement Learning Algorithm with Performance Guarantees
We introduce a new, physics-informed continuous-time reinforcement learning (CT-RL)
algorithm for control of affine nonlinear systems, an area that enables a plethora of well …
algorithm for control of affine nonlinear systems, an area that enables a plethora of well …