Download or read book Design and Analysis of Efficient Reinforcement Learning Algorithms written by Claude-Nicolas Fiechter and published by . This book was released on 1997 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning considers the problem of learning a task or behavior by interacting with one's environment. The learning agent is not explicitly told how the task is to be achieved and has to learn by trial-and-error, using only the rewards and punishments that it receives in response to the actions it takes. In the last ten years there has been a rapidly growing interest in reinforcement learning techniques as a base for intelligent control architectures. Many methods have been proposed and a number of very successful applications have been developed. This dissertation contributes to a theoretical foundation for the study of reinforcement learning by applying some of the methods and tools of computational learning theory to the problem. We propose a formal model of efficient reinforcement learning based on Valiant's Probably Approximately Correct (PAC) learning framework, and use it to design reinforcement learning algorithms and to analyze their performance. We describe the first polynomial-time PAC algorithm for the general finite-state reinforcement learning problem and show that an active and directed exploration of its environment by the learning agent is necessary and sufficient to obtain efficient learning for that problem. We consider the trade-off between exploration and exploitation in reinforcement learning algorithms and show how in general an off-line PAC algorithm can be converted into an on-line algorithm that efficiently balances exploration and exploitation. We also consider the problem of generalization in reinforcement learning and show how in some cases the underlying structure of the environment can be exploited to achieve faster learning. We describe a PAC algorithm for the associative reinforcement learning problem that uses a form of decision lists to represent the policies in a compact way and generalize across different inputs. In addition, we describe a PAC algorithm for a special case of reinforcement learning where the environment can be modeled by a linear system. This particular reinforcement learning problem corresponds to the so-called linear quadratic regulator which is extensively studied and used in automatic and adaptive control.

Download or read book The Design and Analysis of Efficient Learning Algorithms written by Robert E. Schapire and published by MIT Press (MA). This book was released on 1992 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph describes results derived from the mathematically oriented framework of computational learning theory.

Download or read book The Design and Analysis of Efficient Learning Algorithms written by Robert E. Schapire and published by . This book was released on 1991 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis explores various theoretical aspects of machine learning with particular emphasis on techniques for designing and analyzing computationally efficient learning algorithms. Many of the results in this thesis are concerned with a model of concept learning proposed by Valiant. The thesis begins in Chapter 2 with a proof that any 'weak' learning algorithm in this model that performs slightly better than random guessing can be converted into one whose error can be made arbitrarily small. Several interesting consequences of this result are also described. Chapter 3 next explores in detail a simple but powerful technique for discovering the structure of an unknown read-once formula from random examples. An especially nice feature of this technique is its powerful resistance to noise. Chapter 4 considers a realistic extension of the PAC model to concepts that may exhibit uncertain or probabilistic behavior. A range of techniques are explored for designing efficient algorithms for learning such probabilistic concepts. In the last chapter, we present new algorithms for inferring an unknown finite-state automation from its input-output behavior. This problem is motivated by that faced by a robot in unfamiliar surroundings who must, through experimentation, discover the structure of its environment.

Download or read book Algorithms for Reinforcement Learning written by Csaba Szepesvari and published by Morgan & Claypool Publishers. This book was released on 2010 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Efficient Reinforcement Learning written by Zhi Xu (Ph. D.) and published by . This book was released on 2021 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) has recently emerged as a generic yet powerful solution for learning complex decision-making policies, providing the key foundational underpinnings of recent successes in various domains, such as game playing and robotics. However, many state-of-the-art algorithms are data-hungry and computationally expensive, requiring large amounts of data to succeed. While this is possible for certain scenarios, in applications arising in social sciences and healthcare for example, where available data is sparse, this naturally can be costly or infeasible. With the surging interest in applying RL to broader domains, it is imperative to develop an informed view about the usage of data involved in its algorithmic design.

Download or read book Reinforcement Learning Algorithms Analysis and Applications written by Boris Belousov and published by Springer Nature. This book was released on 2021-01-02 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Towards the Understanding of Sample Efficient Reinforcement Learning Algorithms written by Tengyu Xu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL), which aims at designing a suitable policy for an agent via interacting with an unknown environment, has achieved remarkable success in the recent past. Despite its great potential to solve complex tasks, current RL algorithms suffer from requiring a large amount of interaction data, which could result in significant cost in real world applications. Thus, the goal of this thesis is to study the sample complexity of fundamental RL algorithms, and then, to propose new RL algorithms to solve real-world problems with provable efficiency. To achieve this goal, this thesis makes the contributions along the following three main directions: 1. For policy evaluation, we proposed a new on-policy algorithm called variance reduce TD (VRTD) and established the state-of-the-art sample complexity result for off-policy two-timescale TD learning algorithms. 2. For policy optimization, we established improved sample complexity bounds for on-policy actor-critic (AC) type algorithms and proposed the first doubly robust off-policy AC algorithm with provable efficiency guarantee. 3. We proposed three new algorithms: GenTD, CRPO and PARTED to address challenging practical problems of general value function evaluation, safe RL and trajectory-wise reward RL, respectively, with provable efficiency.

Download or read book Model Based Reinforcement Learning written by Milad Farsi and published by John Wiley & Sons. This book was released on 2023-01-05 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal control and dynamic programming – or on algorithms—most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework—from design to application—of a more tractable model-based reinforcement learning technique. Model-Based Reinforcement Learning readers will also find: A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.

Download or read book Efficient Reinforcement Learning with Agent States written by Shi Dong (Researcher of reinforcement learning) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a wide range of decision problems, much focus of academic research has been put on stylized models, whose capacities are usually limited by problem-specific assumptions. In the previous decade, approaches based on reinforcement learning (RL) have received growing attention. With these approaches, a unified method can be applied to a broad class of problems, circumventing the need for stylized solutions. Moreover, when it comes to real-life applications, such RL-based approaches, unfettered from the constraining models, can potentially leverage the growing amount of data and computational resources. As such, continuing innovations might empower RL to tackle problems in the complex physical world. So far, empirical accomplishments of RL have largely been limited to artificial environments, such as games. One reason is that the success of RL often hinges on the availability of a simulator that is able to mass-produce samples. Meanwhile, real environments, such as medical facilities, fulfillment centers, and the World Wide Web, exhibit complex dynamics that can hardly be captured by hard-coded simulators. To bring the achievement of RL into practice, it would be useful to think in terms of how the interactions between the agent and the real world ought to be modeled. Recent works on RL theory tend to focus on restrictive classes of environments that fail to capture certain aspects of the real world. For example, many of such works model the environment as a Markov Decision Process (MDP), which requires that the agent always observe a summary statistic of its situation. In practice, this means that the agent designer has to identify a set of "environmental states, " where each state incorporates all information about the environment relevant to decision-making. Moreover, to ensure that the agent learns from its trajectories, MDP models presume that some environmental states are visited infinitely often. This could be a significant simplification of the real world, as the gifted Argentine poet Jorge Luis Borges once said, "Every day, perhaps every hour, is different." To generate insights on agent design in authentic applications, in this dissertation we consider a more general framework of RL that relaxes such restrictions. Specifically, we demonstrate a simple RL agent that implements an optimistic version of Q-learning and establish through regret analysis that this agent can operate with some level of competence in any environment. While we leverage concepts from the literature on provably efficient RL, we consider a general agent-environment interface and provide a novel agent design and analysis that further develop the concept of agent state, which is defined as the collection of information that the agent maintains in order to make decisions. This level of generality positions our results to inform the design of future agents for operation in complex real environments. We establish that, as time progresses, our agent performs competitively relative to policies that require longer times to evaluate. The time it takes to approach asymptotic performance is polynomial in the complexity of the agent's state representation and the time required to evaluate the best policy that the agent can represent. Notably, there is no dependence on the complexity of the environment. The ultimate per-period performance loss of the agent is bounded by a constant multiple of a measure of distortion introduced by the agent's state representation. Our work is the first to establish that an algorithm approaches this asymptotic condition within a tractable time frame, and the results presented in this dissertation resolve multiple open issues in approximate dynamic programming.

Download or read book Efficient Reinforcement Learning Via Singular Value Decomposition End to end Model based Methods and Reward Shaping written by Clement Gehring and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) provides a general framework for data-driven decision making. However, the very same generality that makes this approach applicable to a wide range of problems is also responsible for its well-known inefficiencies. In this thesis, we consider different properties which are shared by interesting classes of decision making which can be leveraged to design learning algorithms that are both computationally and data efficient. Specifically, this work examines the low-rank structure found in various aspects of decision making problems and the sparsity of effects of classical deterministic planning, as well as the properties that end-to-end model-based methods depend on to perform well. We start by showing how low-rank structure in the successor representation enables the design of an efficient on-line learning algorithm. Similarly, we show how this same structure can be found in the Bellman operator which we use to formulate an efficient variant of the least-squares temporal difference learning algorithm. We further explore low-rank structure in state features to learn efficient transition models which allow for efficient planning entirely in a low dimensional space. We then take a closer look at end-to-end model-based methods in to better understand their properties. We do this by examining this type of approach through the lens of constrained optimization and implicit differentiation. Through the implicit perspective, we derive properties of these methods which allow us to identify conditions under which they perform well. We conclude this thesis by exploring how the sparsity of effects of classical planning problems can used to define general domain-independent heuristics which we can be used to greatly accelerate learning of domain-dependent heuristics through the use of potential-based reward shaping and lifted function approximation.

Download or read book Efficient Reinforcement Learning Through Uncertainties written by Dongruo Zhou and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is centered around the concept of uncertainty-aware reinforcement learning (RL), which seeks to enhance the efficiency of RL by incorporating uncertainty. RL is a vital mathematical framework in the field of artificial intelligence (AI) for creating autonomous agents that can learn optimal behaviors through interaction with their environments. However, RL is often criticized for being sample inefficient and computationally demanding. To tackle these challenges, the primary goals of this dissertation are twofold: to offer theoretical understanding of uncertainty-aware RL and to develop practical algorithms that utilize uncertainty to enhance the efficiency of RL. Our first objective is to develop an RL approach that is efficient in terms of sample usage for Markov Decision Processes (MDPs) with large state and action spaces. We present an uncertainty-aware RL algorithm that incorporates function approximation. We provide theoretical proof that this algorithm achieves near minimax optimal statistical complexity when learning the optimal policy. In our second objective, we address two specific scenarios: the batch learning setting and the rare policy switch setting. For both settings, we propose uncertainty-aware RL algorithms with limited adaptivity. These algorithms significantly reduce the number of policy switches compared to previous baseline algorithms while maintaining a similar level of statistical complexity. Lastly, we focus on estimating uncertainties in neural network-based estimation models. We introduce a gradient-based method that effectively computes these uncertainties. Our approach is computationally efficient, and the resulting uncertainty estimates are both valid and reliable. The methods and techniques presented in this dissertation contribute to the advancement of our understanding regarding the fundamental limits of RL. These research findings pave the way for further exploration and development in the field of decision-making algorithm design.

Download or read book Efficient Learning Machines written by Mariette Awad and published by Apress. This book was released on 2015-04-27 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.

Download or read book Posterior Sampling for Efficient Reinforcement Learning written by Vikranth Reddy Dwaracherla and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning has shown tremendous success over the past few years. Much of this recent success can be attributed to agents learning from an inordinate amount of data in simulated environments. In order to achieve similar success in real environments, it is crucial to address data efficiency. Uncertainty quantification plays a prominent role in designing an intelligent agent which exhibits data efficiency. An agent which has a notion of uncertainty can trade-off between exploration and exploitation and explore in an intelligent manner. Such an agent should not only consider immediate information gain from an action but also its consequences on future learning prospects. An agent which has this capability is said to exhibit deep exploration. Algorithms that tackle deep exploration, so far, have relied on epistemic uncertainty representation through ensembles or other hypermodels, exploration bonuses, or visitation count distributions. An open question is whether deep exploration can be achieved by an incremental reinforcement learning algorithm that tracks a single point estimate, without additional complexity required to account for epistemic uncertainty. We answer this question in the affirmative. In this dissertation, we develop Langevin DQN, a variation of DQN that differs only in perturbing parameter updates with Gaussian noise, and demonstrate through a computational study that Langevin DQN achieves deep exploration. This is the first algorithm that demonstratively achieves deep exploration using a single-point estimate. We also present index sampling, a novel method for efficiently generating approximate samples from a posterior over complex models such as neural networks, induced by a prior distribution over the model family and a set of input-output data pairs. In addition, we develop posterior sampling networks, a new approach to model this distribution over models. We are particularly motivated by the application of our method to tackle reinforcement learning problems, but it could be of independent interest to the Bayesian deep learning community. Our method is especially useful in RL when we use complex exploration schemes, which make use of more than a single sample from the posterior, such as information directed sampling. Finally, we present some preliminary results demonstrating that the Langevin DQN update rule could be used to train posterior sampling networks, as an alternative to index sampling, and further improve data efficiency.

Download or read book Output Feedback Reinforcement Learning Control for Linear Systems written by Syed Ali Asad Rizvi and published by Springer Nature. This book was released on 2022-11-29 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph explores the analysis and design of model-free optimal control systems based on reinforcement learning (RL) theory, presenting new methods that overcome recent challenges faced by RL. New developments in the design of sensor data efficient RL algorithms are demonstrated that not only reduce the requirement of sensors by means of output feedback, but also ensure optimality and stability guarantees. A variety of practical challenges are considered, including disturbance rejection, control constraints, and communication delays. Ideas from game theory are incorporated to solve output feedback disturbance rejection problems, and the concepts of low gain feedback control are employed to develop RL controllers that achieve global stability under control constraints. Output Feedback Reinforcement Learning Control for Linear Systems will be a valuable reference for graduate students, control theorists working on optimal control systems, engineers, and applied mathematicians.

Download or read book FSTTCS 2006 foundations of software technology and theoretical computer science electronic resource written by S. Arun-Kumar and published by Springer Science & Business Media. This book was released on 2006-11-27 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 26th International Conference on the Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2006, held in Kolkata, India, in December 2006. It contains 38 papers that cover a broad variety of current topics from the theory of computing, ranging from formal methods, discrete mathematics, complexity theory, and automata theory to theoretical computer science in general.

Download or read book Design of Experiments for Reinforcement Learning written by Christopher Gatti and published by Springer. This book was released on 2014-11-22 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.

Download or read book Practical Deep Reinforcement Learning with Python written by Ivan Gridin and published by BPB Publications. This book was released on 2022-07-15 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow KEY FEATURES ● Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical. ● Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects. ● Everything is concise, up-to-date, and visually explained with simplified mathematics. DESCRIPTION Reinforcement learning is a fascinating branch of AI that differs from standard machine learning in several ways. Adaptation and learning in an unpredictable environment is the part of this project. There are numerous real-world applications for reinforcement learning these days, including medical, gambling, human imitation activity, and robotics. This book introduces readers to reinforcement learning from a pragmatic point of view. The book does involve mathematics, but it does not attempt to overburden the reader, who is a beginner in the field of reinforcement learning. The book brings a lot of innovative methods to the reader's attention in much practical learning, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical methods. While you understand these techniques in detail, the book also provides a real implementation of these methods and techniques using the power of TensorFlow and PyTorch. The book covers some enticing projects that show the power of reinforcement learning, and not to mention that everything is concise, up-to-date, and visually explained. After finishing this book, the reader will have a thorough, intuitive understanding of modern reinforcement learning and its applications, which will tremendously aid them in delving into the interesting field of reinforcement learning. WHAT YOU WILL LEARN ● Familiarize yourself with the fundamentals of Reinforcement Learning and Deep Reinforcement Learning. ● Make use of Python and Gym framework to model an external environment. ● Apply classical Q-learning, Monte Carlo, Policy Gradient, and Thompson sampling techniques. ● Explore TensorFlow and PyTorch to practice the fundamentals of deep reinforcement learning. ● Design a smart agent for a particular problem using a specific technique. WHO THIS BOOK IS FOR This book is for machine learning engineers, deep learning fanatics, AI software developers, data scientists, and other data professionals eager to learn and apply Reinforcement Learning to ongoing projects. No specialized knowledge of machine learning is necessary; however, proficiency in Python is desired. TABLE OF CONTENTS Part I 1. Introducing Reinforcement Learning 2. Playing Monopoly and Markov Decision Process 3. Training in Gym 4. Struggling With Multi-Armed Bandits 5. Blackjack in Monte Carlo 6. Escaping Maze With Q-Learning 7. Discretization Part II. Deep Reinforcement Learning 8. TensorFlow, PyTorch, and Your First Neural Network 9. Deep Q-Network and Lunar Lander 10. Defending Atlantis With Double Deep Q-Network 11. From Q-Learning to Policy-Gradient 12. Stock Trading With Actor-Critic 13. What Is Next?