Download or read book Data efficient Reinforcement Learning with Self predictive Representations written by Max Schwarzer and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Data efficiency remains a key challenge in deep reinforcement learning. Although modern techniques have been shown to be capable of attaining high performance in extremely complex tasks, including strategy games such as StarCraft, Chess, Shogi, and Go as well as in challenging visual domains such as Atari games, doing so generally requires enormous amounts of interactional data, limiting how broadly reinforcement learning can be applied. In this thesis, we propose SPR, a method drawing from recent advances in self-supervised representation learning designed to enhance the data efficiency of deep reinforcement learning agents. We evaluate this method on the Atari Learning Environment, and show that it dramatically improves performance with limited computational overhead. When given roughly the same amount of learning time as human testers, a reinforcement learning agent augmented with SPR achieves super-human performance on 7 out of 26 games, an increase of 350% over the previous state of the art, while also strongly improving mean and median performance. We also evaluate this method on a set of continuous control tasks, showing substantial improvements over previous methods. Chapter 1 introduces concepts necessary to understand the work presented, including overviews of Deep Reinforcement Learning and Self-Supervised Representation learning. Chapter 2 contains a detailed description of our contributions towards leveraging self-supervised representation learning to improve data-efficiency in reinforcement learning. Chapter 3 provides some conclusions drawn from this work, including a number of proposals for future work.

Download or read book Models for Data efficient Reinforcement Learning on Real world Applications written by Andreas Dörr and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Model Based Reinforcement Learning written by Milad Farsi and published by John Wiley & Sons. This book was released on 2022-12-02 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 Data Efficient Reinforcement Learning with Off policy and Simulated Data written by Josiah Paul Hanna and published by . This book was released on 2019 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning from interaction with the environment -- trying untested actions, observing successes and failures, and tying effects back to causes -- is one of the first capabilities we think of when considering autonomous agents. Reinforcement learning (RL) is the area of artificial intelligence research that has the goal of allowing autonomous agents to learn in this way. Despite much recent success, many modern reinforcement learning algorithms are still limited by the requirement of large amounts of experience before useful skills are learned. Two possible approaches to improving data efficiency are to allow algorithms to make better use of past experience collected with past behaviors (known as off-policy data) and to allow algorithms to make better use of simulated data sources. This dissertation investigates the use of such auxiliary data by answering the question, "How can a reinforcement learning agent leverage off-policy and simulated data to evaluate and improve upon the expected performance of a policy?" This dissertation first considers how to directly use off-policy data in reinforcement learning through importance sampling. When used in reinforcement learning, importance sampling is limited by high variance that leads to inaccurate estimates. This dissertation addresses this limitation in two ways. First, this dissertation introduces the behavior policy gradient algorithm that adapts the data collection policy towards a policy that generates data that leads to low variance importance sampling evaluation of a fixed policy. Second, this dissertation introduces the family of regression importance sampling estimators which improve the weighting of already collected off-policy data so as to lower the variance of importance sampling evaluation of a fixed policy. In addition to evaluation of a fixed policy, we apply the behavior policy gradient algorithm and regression importance sampling to batch policy gradient policy improvement. In the case of regression importance sampling, this application leads to the introduction of the sampling error corrected policy gradient estimator that improves the data efficiency of batch policy gradient algorithms. Towards the goal of learning from simulated experience, this dissertation introduces an algorithm -- the grounded action transformation algorithm -- that takes small amounts of real world data and modifies the simulator such that skills learned in simulation are more likely to carry over to the real world. Key to this approach is the idea of local simulator modification -- the simulator is automatically altered to better model the real world for actions the data collection policy would take in states the data collection policy would visit. Local modification necessitates an iterative approach: the simulator is modified, the policy improved, and then more data is collected for further modification. Finally, in addition to examining them each independently, this dissertation also considers the possibility of combining the use of simulated data with importance sampled off-policy data. We combine these sources of auxiliary data by control variate techniques that use simulated data to lower the variance of off-policy policy value estimation. Combining these sources of auxiliary data allows us to introduce two algorithms -- weighted doubly robust bootstrap and model-based bootstrap -- for the problem of lower-bounding the performance of an untested policy

Download or read book Probabilistic Models for Data Efficient Reinforcement Learning written by Sanket Kamthe and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Reinforcement Learning written by Phil Winder Ph.D. and published by "O'Reilly Media, Inc.". This book was released on 2020-11-06 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website

Download or read book TEXPLORE Temporal Difference Reinforcement Learning for Robots and Time Constrained Domains written by Todd Hester and published by Springer. This book was released on 2013-06-22 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agent’s lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples.

Download or read book Machine Learning ECML 2005 written by João Gama and published by Springer Science & Business Media. This book was released on 2005-09-22 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th European Conference on Machine Learning, ECML 2005, jointly held with PKDD 2005 in Porto, Portugal, in October 2005. The 40 revised full papers and 32 revised short papers presented together with abstracts of 6 invited talks were carefully reviewed and selected from 335 papers submitted to ECML and 30 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.

Download or read book Efficient Reinforcement Learning Using Gaussian Processes written by Marc Peter Deisenroth and published by KIT Scientific Publishing. This book was released on 2010 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.

Download or read book Advancing Data efficiency in Reinforcement Learning written by Sephora Madjiheurem and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Towards Data efficient Deployment of Reinforcement Learning Systems written by Sebastian Schulze and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning and Big Data written by Uma N. Dulhare and published by John Wiley & Sons. This book was released on 2020-09-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.

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 Data Efficient Reinforcement and Transfer Learning in Robotics written by and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.