Practical Reinforcement Learning Using Representation Learning and Safe Exploration for Large Scale Markov Decision Processes

Practical Reinforcement Learning Using Representation Learning and Safe Exploration for Large Scale Markov Decision Processes
Author: Alborz Geramifard
Publisher:
Total Pages: 168
Release: 2012
Genre:
ISBN:
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While creating intelligent agents who can solve stochastic sequential decision making problems through interacting with the environment is the promise of Reinforcement Learning (RL), scaling existing RL methods to realistic domains such as planning for multiple unmanned aerial vehicles (UAVs) has remained a challenge due to three main factors: 1) RL methods often require a plethora of data to find reasonable policies, 2) the agent has limited computation time between interactions, and 3) while exploration is necessary to avoid convergence to the local optima, in sensitive domains visiting all parts of the planning space may lead to catastrophic outcomes. To address the first two challenges, this thesis introduces incremental Feature Dependency Discovery (iFDD) as a representation expansion method with cheap per-timestep computational complexity that can be combined with any online, value-based reinforcement learning using binary features. In addition to convergence and computational complexity guarantees, when coupled with SARSA, iFDD achieves much faster learning (i.e., requires much less data samples) in planning domains including two multi-UAV mission planning scenarios with hundreds of millions of state-action pairs. In particular, in a UAV mission planning domain, iFDD performed more than 12 times better than the best competitor given the same number of samples. The third challenge is addressed through a constructive relationship between a planner and a learner in order to mitigate the learning risk while boosting the asymptotic performance and safety of an agent's behavior. The framework is an instance of the intelligent cooperative control architecture where a learner initially follows a safe policy generated by a planner. The learner incrementally improves this baseline policy through interaction, while avoiding behaviors believed to be risky. The new approach is demonstrated to be superior in two multi-UAV task assignment scenarios. For example in one case, the proposed method reduced the risk by 8%, while improving the performance of the planner up to 30%.

Algorithms for Reinforcement Learning

Algorithms for Reinforcement Learning
Author: Csaba Grossi
Publisher: Springer Nature
Total Pages: 89
Release: 2022-05-31
Genre: Computers
ISBN: 3031015517
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Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration

Algorithms for Reinforcement Learning

Algorithms for Reinforcement Learning
Author: Csaba Szepesvari
Publisher: Morgan & Claypool Publishers
Total Pages: 103
Release: 2010-08-08
Genre: Technology & Engineering
ISBN: 1608454932
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Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration

Recent Advances in Reinforcement Learning

Recent Advances in Reinforcement Learning
Author: Leslie Pack Kaelbling
Publisher: Springer
Total Pages: 286
Release: 2007-08-28
Genre: Computers
ISBN: 0585336563
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Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).

The The Reinforcement Learning Workshop

The The Reinforcement Learning Workshop
Author: Alessandro Palmas
Publisher: Packt Publishing Ltd
Total Pages: 821
Release: 2020-08-18
Genre: Computers
ISBN: 1800209967
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Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

A New Reinforcement Learning Algorithm with Fixed Exploration for Semi-Markov Decision Processes

A New Reinforcement Learning Algorithm with Fixed Exploration for Semi-Markov Decision Processes
Author: Angelo Michael Encapera
Publisher:
Total Pages: 41
Release: 2017
Genre:
ISBN:
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"Artificial intelligence or machine learning techniques are currently being widely applied for solving problems within the field of data analytics. This work presents and demonstrates the use of a new machine learning algorithm for solving semi-Markov decision processes (SMDPs). SMDPs are encountered in the domain of Reinforcement Learning to solve control problems in discrete-event systems. The new algorithm developed here is called iSMART, an acronym for imaging Semi-Markov Average Reward Technique. The algorithm uses a constant exploration rate, unlike its precursor R-SMART, which required exploration decay. The major difference between R-SMART and iSMART is that the latter uses, in addition to the regular iterates of R-SMART, a set of so-called imaging iterates, which form an image of the regular iterates and allow iSMART to avoid exploration decay. The new algorithm is tested extensively on small-scale SMDPs and on large-scale problems from the domain of Total Productive Maintenance (TPM). The algorithm shows encouraging performance on all the cases studied"--Abstract, page iii.

Reinforcement Learning Algorithms: Analysis and Applications

Reinforcement Learning Algorithms: Analysis and Applications
Author: Boris Belousov
Publisher: Springer Nature
Total Pages: 197
Release: 2021-01-02
Genre: Technology & Engineering
ISBN: 3030411885
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This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universität Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.

Deep Reinforcement Learning in Action

Deep Reinforcement Learning in Action
Author: Alexander Zai
Publisher: Manning Publications
Total Pages: 381
Release: 2020-04-28
Genre: Computers
ISBN: 1617295434
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Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
Author: Peter A. Flach
Publisher: Springer
Total Pages: 891
Release: 2012-09-11
Genre: Computers
ISBN: 3642334865
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This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.

Practical Reinforcement Learning

Practical Reinforcement Learning
Author: Engr. S. M. Farrukh Akhtar
Publisher:
Total Pages: 336
Release: 2017-10-17
Genre: Java (Computer program language)
ISBN: 9781787128729
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Master different reinforcement learning techniques and their practical implementation using OpenAI Gym, Python and JavaAbout This Book* Take your machine learning skills to the next level with reinforcement learning techniques* Build automated decision-making capabilities in your systems* Cover Reinforcement Learning concepts, frameworks, algorithms, and more in detailWho This Book Is ForMachine learning/AI practitioners, data scientists, data analysts, machine learning engineers, and developers who are looking to expand their existing knowledge to build optimized machine learning models, will find this book very useful.What You Will Learn* Understand the basics of reinforcement learning methods, algorithms, and more, and the differences between supervised, unsupervised, and reinforcement learning* Master the Markov Decision Process math framework by building an OO-MDP Domain in Java* Learn dynamic programming principles and the implementation of Fibonacci computation in Java* Understand Python implementation of temporal difference learning* Develop Monte Carlo methods and various policies used to build a Monte Carlo simulator using Python* Understand Policy Gradient methods and policies applied in the reinforcement domain* Instill reinforcement methods in the autonomous platform using a moving car example* Apply reinforcement learning algorithms in games with REINFORCEjsIn DetailReinforcement learning (RL) is becoming a popular tool for constructing autonomous systems that can improve themselves with experience. We will break the RL framework into its core building blocks, and provide you with details of each element.This book aims to strengthen your machine learning skills by acquainting you with reinforcement learning algorithms and techniques. This book is divided into three parts. The first part defines Reinforcement Learning and describes its basics. It also covers the basics of Python and Java frameworks, which we are going to use later in the book. The second part discusses learning techniques with basic algorithms such as Temporal Difference, Monte Carlo, and Policy Gradient-all with practical examples. Lastly, in the third part we apply Reinforcement Learning with the most recent and widely used algorithms via practical applications.By the end of this book, you'll know the practical implementation of case studies and current research activities to help you advance further with Reinforcement Learning.Style and approachThis hands-on book will further expand your machine learning skills by teaching you the different reinforcement learning algorithms and techniques using practical examples.