Home page for professor michael kearns, university of. Cs 7642 reinforcement learning and decision making s pr i ng 2019 instructor of record. However, the variance of the performance gradient estimates obtained from the simulation is sometimes excessive. Kavosh asadi, evan cater, dipendra misra, michael l littman september 2019 in neurips workshop on deep reinforcement learning towards a simple approach to multistep modelbased reinforcement learning. Michael littman abstract policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation baxter and bartlett, 2001. He is currently a professor of computer science at brown university. An introduction 2nd edition if you have any confusion about the code or want to report a bug, please open an issue instead of. This tutorial will survey work in this area with an emphasis on recent results. Reinforcement learning via practice and critique advice. Michael littman was born august 30th, 1966, in philadelphia, pennsylvania.
An objectoriented representation for efficient reinforcement learning, carlos diuk, andre cohen and michael l. I rst argue that the framework of reinforcement learning. Michael lederman littman born august 30, 1966 is a computer scientist. Reinforcement learning rl is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. There are also many related courses whose material is available online. Reinforcement learning and simulationbased search in computer go david silver ph. Pdf pac reinforcement learning bounds for rtdp and rand. On policy control with approximation and off policy methods with approximation. Littman1 abstract we examine the impact of learning lipschitz continuous models in the context of modelbased. Littman, booktitle proceedings of the 34th international conference on machine learning, pages 243252, year 2017, editor doina precup and yee whye teh, volume 70, series proceedings of machine learning.
Jan 19, 2010 in modelbased reinforcement learning, an agent uses its experience to construct a representation of the control dynamics of its environment. His research in machine learning examines algorithms for decision making under uncertainty. Hado van hasselt, arthur guez, david silver scaling reinforcement learning toward robocup soccer. It can then predict the outcome of its actions and make decisions that maximize its learning and task performance. Taylor and peter stone journal of machine learning research, volume 10, pp 16331685, 2009. Provably efficient learning with typed parametric models. Such viewpoints are not strictly amenable to proof or refutation, but goal regression raises the possibility that sometimes a 12 species may fruitfully be viewed as a fairly stupid entity. Littman, with 2761 highly influential citations and 361 scientific research papers. It is available for download, but please send me mail if you try it out. Reinforcement learning of local shape in the game of go. This paper surveys the field of reinforcement learning from a computerscience perspective. Proceedings of the eighteenth international conference on machine learning, pp. It examines efficient algorithms, where they exist, for singleagent and multiagent planning as well as approaches to learning nearoptimal decisions from experience. Journal of articial in telligence researc h submitted published reinforcemen t learning a surv ey leslie p ac k kaelbling lpkcsbr o wnedu mic hael l littman.
Perspectives from reinforcement learning, by david abel, a. Deep reinforcement learning with double q learning. Reinforcement learning improves behaviour from evaluative. In advances in neural information processing systems 12 nips, 2000. Both the historical basis of the field and a broad selection of current work are summarized. Near optimal behavior via approximate state abstraction. Incremental learning of planning actions in modelbased reinforcement learning priya dhulipala. Topics include markov decision processes, stochastic and repeated games, partially observable markov decision processes, and reinforcement learning.
Comparisons of several types of function approximators including instancebased like kanerva. Reinforcement learning is the problem faced by an agent that learns behavior through. Convergence results for singlestep onpolicy reinforcementlearning algorithms s singh, t jaakkola, ml littman, c szepesvari machine learning 38 3, 287308, 2000. Convergence results for singlestep onpolicy reinforcementlearning algorithms by satinder singh, tommi jaakkola, michael littman, and csaba. Journal of articial in telligence researc h submitted published. Lipschitz continuity in modelbased reinforcement learning. Michael lederman littman, an american mathematician, computer scientist and professor of cs at brown university, and before at rutgers university and duke university. Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a. Rmax a general polynomial time algorithm for nearoptimal reinforcement learning. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
Hierarchical reinforcement learning is the subfield of rl that deals with the discovery andor exploitation of this underlying structure. Kaelbling littman moore some asp ects of reinforcemen t learning are closely related to searc. We used it in an experiment for carlos dissertation and in a nips 2009 tutorial on modelbased reinforcement learning. Pdf reducing reinforcement learning to kwik online regression. Potentialbased shaping in modelbased reinforcement. Markov games as a framework for multiagent reinforcement learning. Pdf, journal version efficient reinforcement learning in factored mdps. Journal of articial in telligence researc h submitted. Dissertation, university of alberta, edmonton, alberta, canada, 2009. Generalization and scaling in reinforcement learning. Pdf algorithm selection using reinforcement learning.
Littman, booktitle proceedings of the 34th international conference on machine learning, pages 243252, year 2017, editor doina precup and yee whye teh, volume 70, series proceedings of machine learning research, address. Exploring compact reinforcement learning representations with linear regression, thomas j. Reinforcement learning improves behaviour from evaluative feedback. Reinforcement learning for spoken dialogue systems by satinder singh, michael kearns, diane litman and marilyn walker. You have been an academic in ai for more than 25 years during which time you mainly worked on reinforcement learning.
Efficient structure learning in factoredstate mdps alexander l. Reinforcement learning of evaluation functions using temporal differencemonte carlo learning method. A survey, authorleslie pack kaelbling and michael l. Algorithms for sequential decision making ftp directory listing. Littman1 abstract we examine the impact of learning lipschitz continuous models in the context of modelbased reinforcement learning. Transfer learning for reinforcement learning domains. The first one is to break a task into a hierarchy of smaller subtasks, each of which can be learned faster and easier than the whole problem. Greedy algorithms for sparse reinforcement learning hieu le. Littman computational learning theory studies mathematical models that allow one to formally analyze and compare the performance of supervised learning algorithms such as their sample complexity.
A unifying framework for computational reinforcement learning theory by lihong li dissertation director. Pdf reinforcement learning for autonomic network repair. Pdf one of the key problems in reinforcement learning rl is balancing exploration and exploitation. The reinforcement learning rl problem is the challenge of artificial intelligence in a microcosm. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Agentagnostic humanintheloop reinforcement learning. On the computational complexity of stochastic controller optimization in pomdps. Markov games as a framework for multiagent reinforcement. Both the historical basis of the eld and a broad selection of current work are.
In modelbased reinforcement learning, an agent uses its experience to construct a representation of the control dynamics of its environment. It can also be viewed as a learning al gorithm, where the agent improves the value function and policy while acting in an mdp. May 27, 2015 reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a systems ability to make. This tutorial will introduce the fundamental concepts and vocabulary that underlie this field of study. Littman veterans to understand the aims and scope of reinforcement learning research let alone novices in the. An introduction 2nd edition if you have any confusion about the code or want to report a bug, please open an issue instead of emailing me directly. I have a python reinforcement learning demo developed with carlos diuk of the wellknown taxi problem. This course will prepare you to participate in the reinforcement learning research community. This thesis document was submitted to the graduate school at. Journal of articial in telligence researc h submitted published reinforcemen t learning a surv ey leslie p ac k kaelbling lpkcsbr o wnedu mic hael l littman mlittmancsbr o wnedu computer scienc.
He works mainly in reinforcement learning, but has done work in machine learning, game theory, computer networking, partially observable markov decision process solving, computer solving of analogy problems and other areas. Valuefunction reinforcement learning in markov games. David ackley and michael littman to specieslevel learning, and likens individual organisms learning experiences to specieslevel hypothetical thoughts. Littman state abstractions for lifelong reinforcement learning proceedings of the 35th international conference on machine learning, pmlr 80. Alexander kruel interview with michael littman on ai risks.
An alternative softmax operator for reinforcement learning. Potentialbased shaping in modelbased reinforcement learning john asmuth and michael l. In this thesis, i explore the relevance of computational reinforcement learning to the philosophy of rationality and concept formation. Lipschitz continuity in modelbased reinforcement learning kavosh asadi 1 dipendra misra 2 michael l. Realtime dynamic programming rtdp is a popu lar algorithm for planning in a markov decision pro cess mdp. Proceedings of the sixteenth international joint conference on artificial intelligence, morgan kaufmann, 1999, pages 740747. Nearoptimal reinforcement learning in polynomial time satinder singh and michael kearns.
Reinforcement learning is the problem of generating optimal behavior in a sequential decisionmaking environment given the opportunity of interacting with it. Reinforcement learning reinforcement learning satinder singh. Cs 598 statistical reinforcement learning s19 nan jiang. In proceedings of the eleventh international conference on machine learning, pages 157163, san francisco, ca, 1994. Michael littman department of computer science, rutgers. Mar 31, 2020 kavosh asadi, evan cater, dipendra misra, michael l littman september 2019 in neurips workshop on deep reinforcement learning towards a simple approach to multistep modelbased reinforcement learning. Potentialbased shaping in modelbased reinforcement learning.
Littman computational learning theory studies mathematical models that allow one to formally analyze and compare the performance of supervised learning algorithms such as. This is a followup interview with professor of computer science michael littman 12 about artificial intelligence and the possible risks associated with it the interview. In advances in neural information processing systems, vol 2, 1990. Reinforcement learning is a subfield of machine learning, but is also a general purpose formalism for automated decisionmaking and ai. A unified analysis of valuefunctionbased reinforcement. Edu brown university, 115 waterman street, providence, ri 02906 abstract the combinatorial explosion that plagues planning and reinforcement learning rl algorithms. My rutgers students were members of the rutgers laboratory for reallife reinforcement learning or rl 3. This paper surveys the eld of reinforcement learning from a computerscience per spective.
It is written to be accessible to researchers familiar with machine learning. His research interests focus on stochastic games and reinforcement learning along with the related. Markov games as a framework for multiagent reinforcement learning michael l. Michael littman, computer science, rutgers initial explorations of cognitive reinforcement learning. Reinforcement learning midterms due daily show video cs 536. Littman is professor and chair of the department of computer science at rutgers university and directs the rutgers laboratory for reallife reinforcement learning rl3.169 428 62 566 810 462 363 8 1268 380 318 1540 1543 707 1455 1258 246 675 1047 92 628 785 364 874 450 1091 8 752 129 1477