Quantum-enhanced reinforcement learning

Dissertação de mestrado em Engenharia FísicaThe field of Artificial Intelligence has lately witnessed extraordinary results. The ability to design a system capable of beating the world champion of Go, an ancient Chinese game known as the holy grail of AI, caused a spark worldwide, making people believe that some thing revolutionary is about to happen. A different flavor of learning called Reinforcement Learning is at the core of this revolution. In parallel, we are witnessing the emergence of a new field, that of Quantum Machine Learning which has already shown promising results in supervised/unsupervised learning. In this dissertation, we reach for the interplay between Quantum Computing and Reinforcement Learning. This learning by interaction was made possible in the quantum setting using the con cept of oraculization of task environments suggested by Dunjko in 2015. In this dissertation, we extended the oracular instances previously suggested to work in more general stochastic environments. On top of this quantum agent-environment paradigm we developed a novel quantum algorithm for near-optimal decision-making based on the Reinforcement Learn ing paradigm known as Sparse Sampling, obtaining a quantum speedup compared to the classical counterpart. The achievement was a quantum algorithm that exhibits a complexity independent on the number of states of the environment. This independence guarantees its suitability for dealing with large state spaces where planning may be inapplicable. The most important open questions remain whether it is possible to improve the orac ular instances of task environments to deal with even more general environments, especially the ability to represent negative rewards as a natural mechanism for negative feedback instead of some normalization of the reward and the extension of the algorithm to perform an informed tree-based search instead of the uninformed search proposed. Improvements on this result would allow the comparison between the algorithm and more recent classical Reinforcement Learning algorithms.O campo da Inteligência Artificial tem tido resultados extraordinários ultimamente, a capacidade de projetar um sistema capaz de vencer o campeão mundial de Go, um antigo jogo de origem Chinesa, conhecido como o santo graal da IA, causou uma faísca em todo o mundo, fazendo as pessoas acreditarem em que algo revolucionário estar a para acontecer. Um tipo diferente de aprendizagem, chamada Aprendizagem por Reforço está no cerne dessa revolução. Em paralelo surge também um novo campo, o da Aprendizagem Máquina Quântica, que já vem apresentando resultados promissores na aprendizagem supervisionada/não, supervisionada. Nesta dissertação, procuramos invés a interação entre Computação Quântica e a Aprendizagem por Reforço. Esta interação entre agente e Ambiente foi possível no cenário quântico usando o conceito de oraculização de ambientes sugerido por Dunjko em 2015. Neste trabalho, estendemos as instâncias oraculares sugeridas anteriormente para trabalhar em ambientes estocásticos generalizados. Tendo em conta este paradigma quântico agente-ambiente, desenvolvemos um novo algoritmo quântico para tomada de decisão aproximadamente ótima com base no paradigma da Aprendizagem por Reforço conhecido como Amostragem Esparsa, obtendo uma aceleração quântica em comparação com o caso clássico que possibilitou a obtenção de um algoritmo quântico que exibe uma complexidade independente do número de estados do ambiente. Esta independência garante a sua adaptação para ambientes com um grande espaço de estados em que o planeamento pode ser intratável. As questões mais pertinentes que se colocam é se é possível melhorar as instâncias oraculares de ambientes para lidar com ambientes ainda mais gerais, especialmente a capacidade de exprimir recompensas negativas como um mecanismo natural para feedback negativo em vez de alguma normalização da recompensa. Além disso, a extensão do algoritmo para realizar uma procura em árvore informada ao invés da procura não informada proposta. Melhorias neste resultado permitiriam a comparação entre o algoritmo quântico e os algoritmos clássicos mais recentes da Aprendizagem por Reforço

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

The Mathematical Abstraction Theory, The Fundamentals for Knowledge Representation and Self-Evolving Autonomous Problem Solving Systems

The intention of the present study is to establish the mathematical fundamentals for automated problem solving essentially targeted for robotics by approaching the task universal algebraically introducing knowledge as realizations of generalized free algebra based nets, graphs with gluing forms connecting in- and out-edges to nodes. Nets are caused to undergo transformations in conceptual level by type wise differentiated intervening net rewriting systems dispersing problems to abstract parts, matching being determined by substitution relations. Achieved sets of conceptual nets constitute congruent classes. New results are obtained within construction of problem solving systems where solution algorithms are derived parallel with other candidates applied to the same net classes. By applying parallel transducer paths consisting of net rewriting systems to net classes congruent quotient algebras are established and the manifested class rewriting comprises all solution candidates whenever produced nets are in anticipated languages liable to acceptance of net automata. Furthermore new solutions will be added to the set of already known ones thus expanding the solving power in the forthcoming. Moreover special attention is set on universal abstraction, thereof generation by net block homomorphism, consequently multiple order solving systems and the overall decidability of the set of the solutions. By overlapping presentation of nets new abstraction relation among nets is formulated alongside with consequent alphabetical net block renetting system proportional to normal forms of renetting systems regarding the operational power. A new structure in self-evolving problem solving is established via saturation by groups of equivalence relations and iterative closures of generated quotient transducer algebras over the whole evolution.Comment: This article is a part of my thesis giving the unity for both knowledge presentation and self-evolution in autonomous problem solving mathematical systems and for that reason draws heavily from my previous work arxiv:1305.563

Intelligence artificielle et optimisation avec parallélisme

This document is devoted to artificial intelligence and optimization. This part will bedevoted to having fun with high level ideas and to introduce the subject. Thereafter,Part II will be devoted to Monte-Carlo Tree Search, a recent great tool for sequentialdecision making; we will only briefly discuss other tools for sequential decision making;the complexity of sequential decision making will be reviewed. Then, part IIIwill discuss optimization, with a particular focus on robust optimization and especiallyevolutionary optimization. Part IV will present some machine learning tools, useful ineveryday life, such as supervised learning and active learning. A conclusion (part V)will come back to fun and to high level ideas.On parlera ici de Monte-Carlo Tree Search, d'UCT, d'algorithmes évolutionnaires et d'autres trucs et astuces d'IA;l'accent sera mis sur la parallélisation

Glossarium BITri 2016 : Interdisciplinary Elucidation of Concepts, Metaphors, Theories and Problems Concerning Information

222 p.Terms included in this glossary recap some of the main concepts, theories, problems and metaphors concerning INFORMATION in all spheres of knowledge. This is the first edition of an ambitious enterprise covering at its completion all relevant notions relating to INFORMATION in any scientific context. As such, this glossariumBITri is part of the broader project BITrum, which is committed to the mutual understanding of all disciplines devoted to information across fields of knowledge and practic

Navigation

Reihe Begriffe des digitalen Bildes Das DFG-Schwerpunktprogramm ‚Das digitale Bild‘ untersucht von einem multiperspektivischen Standpunkt aus die zentrale Rolle, die dem Bild im komplexen Prozess der Digitalisierung des Wissens zukommt. In einem deutschlandweiten Verbund soll dabei eine neue Theorie und Praxis computerbasierter Bildwelten erarbeitet werden

Unfolding Imagos: an inquiry into the aesthetics of Action-Phenomenology

In modern civilisation, magic in its instrumental (sorcerous) sense would appear to have been completely superseded by science, but that should not blind us to the (arguably) reliable efficacy of invocation, nor to the metaphysical implication of this efficacy–that it points to the psychophysical nature of reality.3 This thesis is an inquiry into the use of imagination as being restorative of identity. Working experimentally with poetic-aesthetic method—writings initially, then visual images—I use altered states of mind, and access to the otherworldly, in order to offer re-arrangements of local realities. Preoccupied as most people are with everyday realities, radical proposals—animism, enchantment, non-ordinary ways of knowing and being— don’t often find room: in our everyday lives, workplaces, relationships; or in action-inquiry. The body of this inquiry reflects the qualities of what Bachelard terms an immense philosophical daydream.4 My claim in-depth is, firstly that working with poetic-aesthetic method in this way is restorative: of individual, groups, societies; secondly, that the framings offered in Part V Light are the bases for further in depth research. Initially proposed as inquiry into the healing of disrupted identity (a consequence of organisational and procedural abuse), the focus of inquiry shifts, unfolds. Inquiry into writing, poetry, aesthetics gives way to a deeper inquiry into connectedness; uncovering healing engendered by Seeing connections: to the morethan- human world (animism), the otherworldly (enchantment). Questions of knowing and being surface, along with how to relate these back to the world. In A Language Older Than Words, Jensen relates a story of connecting a plant—a dracaena cane—to a polygraph. The story relates the plant’s responses to a researcher imagining harming it; plant becoming attuned to human; yoghurt responding to death of remote microbes. This leads to altered ways of knowing and being not often in our consciousness; preoccupied as we are with everyday realities.5 Atelier—a series of experimental practices—provokes deeper inquiry: into the nature and frameworks of inquiry, and, ultimately, theory. The problem, the contradiction the scientists are stuck with, is that of mind. Mind has no matter or energy but they can’t escape its predominance over everything they do. Logic exists in the mind. Numbers exist only in the mind. I don’t get upset when they say that ghosts exist in the mind. It’s that only that gets me. Science is only in your mind too, its just that that doesn’t make it bad. Or ghosts either.6 Experience of trauma, abuse, offers distortions of mind and self. These distortions are ascribed as illness but provoked through the deepening inquiry of a series of experimental practices: referred to in this work as The Atelier. I come to suggest that this is a problem of mind; and of our relationship to the unscientific. Playing with these distortions unfolds access to rarely accessed realms: of consciousness; of seeing. Inquiring into these fields of identity reveals new putative fields: Imago-Unfolding; Via Arbora; 4th-Person Inquiry; Action- Phenomenology. These fields occur—in layers—throughout this text, and in mind