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

https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository

Genetic Programming: On the Programming of Computers by Means of Natural Selection

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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://hal.inria.fr/hal-00965161/document

Workshops of the Fifth International Brain-Computer

Covers such issues as the meaning of representation, the role representation plays in programs for learning control, expressive power of particular representation schemes, and the impact of choosing a particular representation on the complexity of reasoning and learning. The authors cover intelligence-in-the limb versus intelligence; learning efficiency as the critical issue in learning control, selective versus constructive induction and map learning: binary- and fuzzy-logic-base knowledge representation for intelligent robotic systems; and knowledge representation, definability, and self-reference. >

Knowledge representations for learning control

The topic of this annotated bibliography is connectionism, a field of computer science that has enjoyed a vast resurgence in the last ten years. Properly speaking, connectionism should be regarded as part of Artificial Intelligence, or AI, and up to some years ago it was usually so treated. The earlier entries below will make that clear. Another term for connectionism is Neural Networks, and it is being widely used, especially among the new efforts that are arising, including start-up companies.

Selected bibliography on connectionism

Machine learning in the healthcare domain is often hindered by data which are both noisy and lacking reliable ground truth labeling. Moreover, the cost of cleaning and annotating this data is significant since, unlike other data domains, medical data annotation requires the work of skilled medical professionals. In this work, we introduced the use of reinforcement learning to mimic the decision-making process of annotators for medical events allowing automation of annotation and labeling. Our reinforcement agent learns to annotate health monitor alarm data based on annotations done by an expert. We demonstrate the efficacy of our implementation on ICU critical alarm data sets. We evaluate our algorithm against standard supervised machine learning and deep learning methods. Compared to SVM and LSTM methods, our method achieves high sensitivity that is critical for alarm data; exhibits better generalization across mixed downsampling; and preserves comparable model performance.

https://link.springer.com/content/pdf/10.1007/s42979-020-00356-z.pdf

AI-Assisted Annotator Using Reinforcement Learning

Feedforward neural networks are trained to classify half-second segments of six-channel, EEG data into one of five classes corresponding to five mental tasks performed by one subject. Two and three-layer neural networks are trained on a 128-processor SIMD computer using 10-fold cross-validation and early stopping to limit over-fitting. Four representations of the EEG signals, based on autoregressive (AR) models and Fourier Transforms, are compared. Using the AR representation and averaging over consecutive segments, an average of 72% of the test segments are correctly classified; for some test sets 100% are correctly classified. Cluster arm, is of the resulting hidden-unit weight vectors suggests which electrodes and representation components are the most relevant to the classification problem.

Charles W. Anderson

Papers

Workshops of the Fifth International Brain-Computer

Knowledge representations for learning control

Selected bibliography on connectionism

AI-Assisted Annotator Using Reinforcement Learning

Identifying mental tasks from spontaneous EEG: Signal representation and spatial analysis