A tutorial on optimal control and reinforcement learning methods for ...

Learning-based Optimal Control of Time-Varying Linear Systems ...

... optimal solution to the control issue. In the following sections, we demonstrate how applying a reinforcement learning method makes it feasible ...

A Generalized Path Integral Control Approach to Reinforcement ...

optimal control and dynamic programming with modern learning techniques from statistical esti- mation theory. In this vein, this paper suggests to use the ...

How to learn optimal control theory from scratch - Quora

For learning Reinforcement Learning, there are a number of good references: Udacity MOOC (Reinforcement Learning) or watch David Silver's ...

A tutorial survey of reinforcement learning | Sādhanā

Research on reinforcement learning during the past decade has led to the development of a variety of useful algorithms. This paper surveys the literature and ...

Optimal Control and Reinforcement Learning of Switched Systems

Instead of directly applying existing neural network based algorithms, we develop a distinct Q-learning algorithm which explicitly incorporates the analytical ...

Learning-Based Control: A Tutorial and Some Recent Results

An entanglement of techniques from reinforcement learning and model-based control theory is advocated to find a sequence of suboptimal ...

Reinforcement Learning and Optimal Control - Free Computer Books

This book focuses on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. It gives a fairly comprehensive ...

Convex Q-Learning, Part 1: Deterministic Optimal Control - NASA/ADS

The challenge seems paradoxical, given the long history of convex analytic approaches to dynamic programming. The paper begins with a brief survey of linear ...

Dynamic Programming and Optimal Control - Athena Scientific

I also has a full chapter on suboptimal control and many related techniques, such as open-loop feedback controls, limited lookahead policies, rollout algorithms ...

Quantum optimal control in quantum technologies. Strategic report ...

Quantum optimal control, a toolbox for devising and implementing the shapes of external fields that accomplish given tasks in the operation ...

Reinforcement Learning - Simons Institute

Lewis, editor, Reinforcement Learning and Approximate Dynamic. Programming for Feedback Control. Wiley, 2011. [26] A. M. Devraj, A. Bušic, and S. Meyn.

Tutorial on Model-Based Methods in Reinforcement Learning

The aim of this tutorial is to make model-based methods more recognized and accessible to the machine learning community. Given recent successful applications ...

Reinforcement learning - GeeksforGeeks

Reinforcement Learning (RL) is a branch of machine learning focused on making decisions to maximize cumulative rewards in a given situation.

ICML 2008 Tutorials Schedule, Saturday 5 July

Special emphasis is given on newer approaches of using inference techniques to solving stochastic optimal control problems. The tutorial is introductory and ...

Reinforcement Learning Model-Based and Model-Free Paradigms ...

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis ...

Learning-Based Control: A Tutorial and Some Recent Results

Different from other machine learning (ML) techniques, this learning architecture is especially useful when the learning objective is to find the optimal. 3 ...

How to get started on learning optimal control (with regards ... - Quora

For learning Reinforcement Learning, there are a number of good references: * Udacity MOOC (Reinforcement Learning) or watch David Silver's ...

Control of Qubit Dynamics Using Reinforcement Learning - INSPIRE

This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in a single qubit.

Optimal Adaptive Control and Differential Games by Reinforcement ...

ADP is a reinforcement machine learning technique that is motivated by learning mechanisms in biological and animal systems. It is connected ...

Optimal Control with OpenAI Gym - Towards Data Science

For reinforcement learning, we don't need any prior knowledge of our system. A reinforcement learning algorithm can learn a model of the ...