Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

/pdf/reinforcement-learning-an-introduction-rzxgej9p17.pdf

Reinforcement Learning: An Introduction

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This action-based or reinforcement learning can capture notions of optimal behavior occurring in natural systems. We describe mathematical formulations for reinforcement learning and a practical implementation method known as adaptive dynamic programming. These give us insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior.

Reinforcement learning and adaptive dynamic programming for feedback control

Foreword. 1. ADP: goals, opportunities and principles. Part I: Overview. 2. Reinforcement learning and its relationship to supervised learning. 3. Model-based adaptive critic designs. 4. Guidance in the use of adaptive critics for control. 5. Direct neural dynamic programming. 6. The linear programming approach to approximate dynamic programming. 7. Reinforcement learning in large, high-dimensional state spaces. 8. Hierarchical decision making. Part II: Technical advances. 9. Improved temporal difference methods with linear function approximation. 10. Approximate dynamic programming for high-dimensional resource allocation problems. 11. Hierarchical approaches to concurrency, multiagency, and partial observability. 12. Learning and optimization - from a system theoretic perspective. 13. Robust reinforcement learning using integral-quadratic constraints. 14. Supervised actor-critic reinforcement learning. 15. BPTT and DAC - a common framework for comparison. Part III: Applications. 16. Near-optimal control via reinforcement learning. 17. Multiobjective control problems by reinforcement learning. 18. Adaptive critic based neural network for control-constrained agile missile. 19. Applications of approximate dynamic programming in power systems control. 20. Robust reinforcement learning for heating, ventilation, and air conditioning control of buildings. 21. Helicopter flight control using direct neural dynamic programming. 22. Toward dynamic stochastic optimal power flow. 23. Control, optimization, security, and self-healing of benchmark power systems.

/pdf/handbook-of-learning-and-approximate-dynamic-programming-3i2odqjzkg.pdf

Handbook of Learning and Approximate Dynamic Programming

This paper focuses on a systematic treatment for developing a generic online learning control system based on the fundamental principle of reinforcement learning or more specifically neural dynamic programming. This online learning system improves its performance over time in two aspects: 1) it learns from its own mistakes through the reinforcement signal from the external environment and tries to reinforce its action to improve future performance; and 2) system states associated with the positive reinforcement is memorized through a network learning process where in the future, similar states will be more positively associated with a control action leading to a positive reinforcement. A successful candidate of online learning control design is introduced. Real-time learning algorithms is derived for individual components in the learning system. Some analytical insight is provided to give guidelines on the learning process took place in each module of the online learning control system.

Online learning control by association and reinforcement

This paper focuses on a systematic treatment for developing a generic online learning control system based on the fundamental principle of reinforcement learning or more specifically neuro-dynamic programming. This real time learning system improves its performance over time in two aspects: it learns from its own mistakes through the reinforcement signal from the external environment and try to reinforce its action to improve future performance; and system's state associated with the positive reinforcement is memorized through a network learning process where in the future, similar states will be more positively associated with a control action leading to a positive reinforcement. Two successful candidates of online learning control designs are introduced. Real time learning algorithms can be derived for individual components in the learning system. Some analytical insights are provided to give some guidelines on the entire online learning control system.

http://www.derongliu.org/adp/adp-cdrom/c-Si-Wang2001.pdf

This chapter focuses on presenting some key concepts of machine learning, approximate dynamic programming, and the relationships between them. Discussion and comparisons are made based on various aspects of the two fields such as training information, behavioral variety, problem conversion, applicable tasks, and so forth. This chapter mentions many real-world examples to illustrate some of the important distinctions being made. The primary focus of this chapter is a discussion of the concepts and strategies of machine learning, not necessarily algorithmic details. This chapter provides high-level perspective on machine learning and approximate dynamic programming.

/pdf/reinforcement-learning-and-its-relationship-to-supervised-1beiuq015y.pdf

Jennie Si

Papers

Handbook of Learning and Approximate Dynamic Programming

Online learning control by association and reinforcement

Online learning control by association and reinforcement

Reinforcement Learning and Its Relationship to Supervised Learning

Supervised Actor-Critic Reinforcement Learning