Download or read book Efficient Exploration of Reinforcement Learning in Non stationary Environments with More Complex State Dynamics written by Parker Ruochen Hao and published by . This book was released on 2020 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploration technique is the key to reach optimal results via reinforcement learning in a time-ecient manner. When reinforcement learning was first proposed, exploration was implemented as randomly choosing across the action space, resulting in potentially exponential number of state-action pairs to explore from. Over the years, more ecient exploration techniques were proposed, allowing faster convergence and delivering better results across different domains of applications. With the growing interest in non-stationary environments, some of those exploration techniques are explored where the optimal state-action changes across dierent periods of learning process. In the past, those techniques have performed well in control setups where the targets are non-stationary and continuously moving. However, such techniques have not been extensively tested in environments involving jumps or non-continuous regime changes. This paper analyzes methods for achieving comparable exploration performance under such challenging environments and proposes new techniques for the agent to capture the regime changes of non-stationary environments as more complex states or intrinsic rewards.

Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. 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 while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

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 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 Safety Risk Awareness and Exploration in Reinforcement Learning written by Teodor Mihai Moldovan and published by . This book was released on 2014 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: Replicating the human ability to solve complex planning problems based on minimal prior knowledge has been extensively studied in the field of reinforcement learning. Algorithms for discrete or approximate models are supported by theoretical guarantees but the necessary assumptions are often constraining. We aim to extend these results in the direction of practical applicability to more realistic settings. Our contributions are restricted to three specific aspects of practical problems that we believe to be important when applying reinforcement learning techniques: risk awareness, safe exploration and data efficient exploration. Risk awareness is important in planning situations where restarts are not available and performance depends on one-off returns rather than average returns. The expected return is no longer an appropriate objective because the law of large numbers does not apply. In Chapter 2 we propose a new optimization objective for risk-aware planning and show that it has desirable theoretical properties, relating it to previously proposed risk-aware objectives: minmax, exponential utility, percentile and mean minus variance. In environments with uncertain dynamics, exploration is often necessary to improve performance. Existing reinforcement learning algorithms provide theoretical exploration guarantees, but they tend to rely on the assumption that any state is eventually reachable from any other state by following a suitable policy. For most physical systems this assumption is impractical as the systems would break before any reasonable exploration has taken place. In Chapter 3 we address the need for a safe exploration method. In Chapter 4 we address the specific challenges presented by extending model-based reinforcement learning methods from discrete to continuous dynamical systems. System representations based on explicitly enumerated states are not longer applicable. To address this challenge we use a Dirichlet process mixture of linear models to represent dynamics. The proposed model strikes a good balance between compact representation and flexibility. To address the challenge of efficient exploration-exploitation trade-off we apply the principle of Optimism in the Face of Uncertainty that underlies numerous other provably efficient algorithms in simpler settings. Our algorithm reduces the exploration problem to a sequence of classical optimal control problems. Synthetic experiments illustrate the effectiveness of our methods.

Download or read book Reinforcement Learning and Dynamic Programming Using Function Approximators written by Lucian Busoniu and published by CRC Press. This book was released on 2017-07-28 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.

Download or read book Non Stationary Reinforcement Learning written by Wang Chi Cheung and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated by applications in inventory control and real-time bidding, we consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under temporal drifts. In this setting, both the reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain emph{variation budgets}. We first develop the Sliding Window Upper-Confidence bound for Reinforcement Learning with Confidence Widening ( texttt{SWUCRL2-CW}) algorithm, and establish its emph{dynamic regret} bound when the variation budgets are known. In addition, we propose the Bandit-over-Reinforcement Learning ( texttt{BORL}) algorithm to adaptively tune the sw~to achieve the same dynamic regret bound, but in a emph{parameter-free} manner, ie, without knowing the variation budgets. Finally, we conduct numerical experiments to show that our proposed algorithms achieve superior empirical performance compared with existing algorithms. Notably, under non-stationarity, historical data samples may falsely indicate that state transition rarely happens. This thus presents a significant challenge when one tries to apply the conventional Optimism in the Face of Uncertainty (OFU) principle to achieve low dynamic regret bound our problem. We overcome this challenge by proposing a novel confidence widening technique that incorporates additional optimism into our learning algorithms. To extend our theoretical findings, we demonstrate, in the context of single item inventory control with lost-sales, fixed cost, and zero-lead time, how one can leverage special structures on the state transition distributions to bypass the difficulty of exploring time-varying demand environments.

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 Theory Of Optimal Experiments written by V.V. Fedorov and published by Elsevier. This book was released on 2013-04-20 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory Of Optimal Experiments

Download or read book Learning to Teach and Meta learning for Sample efficient Multiagent Reinforcement Learning written by Dong Ki Kim (S.M.) and published by . This book was released on 2020 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning optimal policies in the presence of non-stationary policies of other simultaneously learning agents is a major challenge in multiagent reinforcement learning (MARL). The difficulty is further complicated by other challenges, including the multiagent credit assignment, the high dimensionality of the problems, and the lack of convergence guarantees. As a result, many experiences are often required to learn effective multiagent policies. This thesis introduces two frameworks to reduce the sample complexity in MARL. The first framework presented in this thesis provides a method to reduce the sample complexity by exchanging knowledge between agents. In particular, recent work on agents that learn to teach other teammates has demonstrated that action advising accelerates team-wide learning. However, the prior work simplified the learning of advising policies by using simple function approximations and only considering advising with primitive (low-level) actions, both of which limit the scalability of learning and teaching to more complex domains. This thesis introduces a novel learning-to-teach framework, called hierarchical multiagent teaching (HMAT), that improves scalability to complex environments by using a deep representation for student policies and by advising with more expressive extended-action sequences over multiple levels of temporal abstraction. Our empirical evaluations demonstrate that HMAT improves team-wide learning progress in large, complex domains where previous approaches fail. HMAT also learns teaching policies that can effectively transfer knowledge to different teammates with knowledge of different tasks, even when the teammates have heterogeneous action spaces. The second framework introduces the first policy gradient theorem based on meta-learning, which enables fast adaptation (i.e., need only a few iterations) with respect to the non-stationary fellow agents in MARL. The policy gradient theorem that we prove inherently includes both a self-shaping term that considers the impact of a meta-agent's initial policy on its adapted policy and an opponent-shaping term that exploits the learning dynamics of the other agents. We demonstrate that our meta-policy gradient provides agents to meta-learn about different sources of non-stationarity in the environment to improve their learning performances.

Download or read book Reinforcement Learning in Non stationary Environments written by Erwan Lecarpentier and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: How should an agent act in the face of uncertainty on the evolution of its environment?In this dissertation, we give a Reinforcement Learning perspective on the resolution of nonstationaryproblems. The question is seen from three different aspects. First, we study theplanning vs. re-planning trade-off of tree search algorithms in stationary Markov DecisionProcesses. We propose a method to lower the computational requirements of such an algorithmwhile keeping theoretical guarantees on the performance. Secondly, we study thecase of environments evolving gradually over time. This hypothesis is expressed through amathematical framework called Lipschitz Non-Stationary Markov Decision Processes. Wederive a risk averse planning algorithm provably converging to the minimax policy in thissetting. Thirdly, we consider abrupt temporal evolution in the setting of lifelong ReinforcementLearning. We propose a non-negative transfer method based on the theoretical study ofthe optimal Q-function's Lipschitz continuity with respect to the task space. The approachallows to accelerate learning in new tasks. Overall, this dissertation proposes answers to thequestion of solving Non-Stationary Markov Decision Processes under three different settings.

Download or read book Active Inference written by Thomas Parr and published by MIT Press. This book was released on 2022-03-29 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning.

Download or read book Learning Representations in Reinforcement Learning written by Jacob Rafati Heravi and published by . This book was released on 2019 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement Learning (RL) algorithms allow artificial agents to improve their action selection policy to increase rewarding experiences in their environments. Temporal Difference (TD) learning algorithm, a model-free RL method, attempts to find an optimal policy through learning the values of agent's actions at any state by computing the expected future rewards without having access to a model of the environment. TD algorithms have been very successful on a broad range of control tasks, but learning can become intractably slow as the state space grows. This has motivated methods for using parameterized function approximation for the value function and developing methods for learning internal representations of the agent's state, to effectively reduce the size of state space and restructure state representations in order to support generalization. This dissertation investigates biologically inspired techniques for learning useful state representations in RL, as well as optimization methods for improving learning. There are three parts to this investigation. First, failures of deep RL algorithms to solve some relatively simple control problems are explored. Taking inspiration from the sparse codes produced by lateral inhibition in the brain, this dissertation offers a method for learning sparse state representations. Second, the challenges of RL in efficient exploration of environments with sparse delayed reward feedback, as well as the scalability issues in large-scale applications are addressed. The hierarchical structure of motor control in the brain prompts the consideration of approaches to learning action selection policies at multiple levels of temporal abstraction. That is learning to select subgoals separately from action selection policies that achieve those subgoals. This dissertation offers a novel model-free Hierarchical Reinforcement Learning framework, including approaches to automatic subgoal discovery based on unsupervised learning over memories of past experiences. Third, more complex optimization methods than those typically used in deep learning, and deep RL are explored, focusing on improving learning while avoiding the need to fine tune many hyperparameters. This dissertation offers limited-memory quasi-Newton optimization methods to efficiently solve highly nonlinear and nonconvex optimization problems for deep learning and deep RL applications. Together, these three contributions provide a foundation for scaling RL to more complex control problems through the learning of improved internal representations.

Download or read book Discrete State action Representations for Hierarchical Reinforcement Learning written by Diego Fernando Gómez Noriega and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: There is increasing evidence that hierarchical reinforcement learning methods provide better control laws than standard reinforcement learning methods, and even more so that it is necessary to solve complex control tasks for which efficient exploration is imperative. We propose a new hierarchical reinforcement learning method inspired in the way humans, and probably most animals, seem to execute complex tasks: by generating discrete mental representations that allow for efficient planning and decision taking. More specifically, our approach consists in the use of probabilistic generative models as discrete abstractions of the state space, an inference process of this models, and a high-level decision method that maps the chosen models to high-level actions. The high-level action works as command to a low-level controller that also uses the full sensory information of the state to take an action. We implemented our method on two continuous environments of the OpenAI Gym benchmark suite and compared it with two non-hierarchical state-of-the-art methods. Our results indicate that using the proposed hierarchical method provides a significant advantage in learning efficiency and allows capturing useful representations for control.

Download or read book Teamwork and Exploration in Reinforcement Learning written by Lucas Cassano and published by . This book was released on 2020 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) is a powerful machine learning paradigm that studies the interaction between a single agent with an unknown environment. A plethora of applications fit into the RL framework, however, in many cases of interest, a team of agents will need to interact with the environment and with each other to achieve a common goal. This is the object study of collaborative multi-agent RL (MARL). Several challenges arise when considering collaborative MARL. One of these challenges is decentralization. In many cases, due to design constraints, it is undesirable or inconvenient to constantly relay data between agents and a centralized location. Therefore, fully distributed solutions become preferable. The first part of this dissertation addresses the challenge of designing fully decentralized MARL algorithms. We consider two problems: policy evaluation and policy optimization. In the policy evaluation problem, the objective is to estimate the performance of a target team policy in a particular environment. This problem has been studied before for the case with streaming data, however, in most implementations the target policy is evaluated using a finite data set. For this case, existing algorithms guarantee convergence at a sub-linear rate. In this dissertation we introduce Fast Diffusion for Policy Evaluation (FDPE), an algorithm that converges at linear rate for the finite data set case. We then consider the policy optimization problem, where the objective is for all agents to learn an optimal team policy. This problem has also been studied recently, however, existing solutions are data inefficient and converge to Nash equilibria (whose performance can be catastrophically bad) as opposed to team optimal policies. For this case we introduce the Diffusion for Team Policy Optimization (DTPO) algorithm. DTPO is more data efficient than previous algorithms and does not converge to Nash equilibria. For both of these cases, we provide experimental studies that show the effectiveness of the proposed methods. Another challenge that arises in collaborative MARL, which is orthogonal to the decentralization problem, is that of scalability. The parameters that need to be estimated when full team policies are learned, grow exponentially with the number of agents. Hence, algorithms that learn joint team policies quickly become intractable. A solution to this problem is for each agent to learn an individual policy, such that the resulting joint team policy is optimal. This problem has been the object of much research lately. However, most solution methods are data inefficient and often make unrealistic assumptions that greatly limit the applicability of these approaches. To address this problem we introduce Logical Team Q-learning (LTQL), an algorithm that learns factored policies in a data efficient manner and is applicable to any cooperative MARL environment. We show that LTQL outperforms previous methods in a challenging predator-prey task. Another challenge is that of efficient exploration. This is a problem both in the single-agent and multi-agent settings, although in MARL it becomes more severe due to the larger state-action space. The challenge of deriving policies that are efficient at exploring the state space has been addressed in many recent works. However, most of these approaches rely on heuristics, and more importantly, they consider the problem of exploring the state space separately from that of learning an optimal policy (even though they are related, since the state-space is explored to collect data to learn an optimal policy). To address this challenge, we introduce the Information Seeking Learner (ISL), an algorithm that displays state of the art performance in difficult exploration benchmarks. The fundamental value of our work on exploration is that we take a fundamentally different approach from previous works. As opposed to earlier methods we consider the problem of exploring the state space and learning an optimal policy jointly. The main insight of our approach is that in RL, obtaining point estimates of the quantities of interest is not sufficient and confidence bound estimates are also necessary.

Download or read book Mixture Weighted Policy Cover written by Dylan Miller and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploration plays a major role in the performance of reinforcement learning algorithms. Successful exploration should force the agent to access parts of the state-action space that it has not been heavily exposed to. This allows agents to find potentially better trajectories in terms of the value function that they yield. Exploration becomes much more difficult however when the environment is nonstationary. This is the case in multiagent reinforcement learning where other agents also learn and so change the dynamics of the environment from the perspective of any single agent. The upper confidence bound style reward bonus that is common in many reinforcement learning algorithms does not take this nonstationarity into account and therefore cannot be successfully applied to the multiagent setting. In this thesis, we propose Mixture-Weighted Policy Cover, a policy iteration algorithm using an upper confidence bound based intrinsic exploration bonus that encourages exploration in episodic multiagent settings by defining a policy cover that favors newer policies.

Download or read book Lifelong Machine Learning Second Edition written by Zhiyuan Sun and published by Springer Nature. This book was released on 2022-06-01 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.