Hado van hasselt, arthur guez, david silver scaling reinforcement learning toward robocup soccer. This course will prepare you to participate in the reinforcement learning research community. Littman1 abstract we examine the impact of learning lipschitz continuous models in the context of modelbased reinforcement learning. 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. Valuefunction reinforcement learning in markov games. His research interests focus on stochastic games and reinforcement learning along with the related. Kaelbling littman moore some asp ects of reinforcemen t learning are closely related to searc. Agentagnostic humanintheloop reinforcement learning. A unifying framework for computational reinforcement learning theory by lihong li dissertation director. Edu brown university, 115 waterman street, providence, ri 02906 abstract the combinatorial explosion that plagues planning and reinforcement learning rl algorithms.
Home page for professor michael kearns, university of. I have a python reinforcement learning demo developed with carlos diuk of the wellknown taxi problem. Hierarchical reinforcement learning is the subfield of rl that deals with the discovery andor exploitation of this underlying structure. In advances in neural information processing systems 12 nips, 2000. Alexander kruel interview with michael littman on ai risks. 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. Reinforcement learning is the problem faced by an agent that learns behavior through. Pdf pac reinforcement learning bounds for rtdp and rand. In modelbased reinforcement learning, an agent uses its experience to construct a representation of the control dynamics of its environment. On the computational complexity of stochastic controller optimization in pomdps.
An objectoriented representation for efficient reinforcement learning, carlos diuk, andre cohen and michael l. Markov games as a framework for multiagent reinforcement. Littman veterans to understand the aims and scope of reinforcement learning research let alone novices in the. Reinforcement learning of local shape in the game of go. Pdf reinforcement learning for autonomic network repair. You will also have the opportunity to learn from two of the foremost experts in this field of research, profs. I rst argue that the framework of reinforcement learning. An introduction 2nd edition if you have any confusion about the code or want to report a bug, please open an issue instead of. Michael littman department of computer science, rutgers. It examines efficient algorithms, where they exist, for singleagent and multiagent planning as well as approaches to learning nearoptimal decisions from experience. Pdf algorithm selection using reinforcement learning.
Perspectives from reinforcement learning, by david abel, a. Proceedings of the sixteenth international joint conference on artificial intelligence, morgan kaufmann, 1999, pages 740747. Pdf one of the key problems in reinforcement learning rl is balancing exploration and exploitation. 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. Greedy algorithms for sparse reinforcement learning hieu le. Efficient structure learning in factoredstate mdps alexander l. Littman state abstractions for lifelong reinforcement learning proceedings of the 35th international conference on machine learning, pmlr 80.
Convergence results for singlestep onpolicy reinforcementlearning algorithms s singh, t jaakkola, ml littman, c szepesvari machine learning 38 3, 287308, 2000. 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. Realtime dynamic programming rtdp is a popu lar algorithm for planning in a markov decision pro cess mdp. Generalization and scaling in reinforcement learning. Exploring compact reinforcement learning representations with linear regression, thomas j. Potentialbased shaping in modelbased reinforcement learning john asmuth and michael l.
Comparisons of several types of function approximators including instancebased like kanerva. Pdf, journal version efficient reinforcement learning in factored mdps. 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. Algorithms for sequential decision making ftp directory listing.
We used it in an experiment for carlos dissertation and in a nips 2009 tutorial on modelbased reinforcement learning. Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a. David ackley and michael littman to specieslevel learning, and likens individual organisms learning experiences to specieslevel hypothetical thoughts. This tutorial will introduce the fundamental concepts and vocabulary that underlie this field of study. Deep reinforcement learning with double q learning.
Reinforcement learning improves behaviour from evaluative feedback. Topics include markov decision processes, stochastic and repeated games, partially observable markov decision processes, and reinforcement learning. An alternative softmax operator for reinforcement learning. 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. His research in machine learning examines algorithms for decision making under uncertainty. Reinforcement learning reinforcement learning satinder singh.
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. Incremental learning of planning actions in modelbased reinforcement learning priya dhulipala. Cs 598 statistical reinforcement learning s19 nan jiang. Lipschitz continuity in modelbased reinforcement learning kavosh asadi 1 dipendra misra 2 michael l. Cs 7642 reinforcement learning and decision making s pr i ng 2019 instructor of record. It can also be viewed as a learning al gorithm, where the agent improves the value function and policy while acting in an mdp. 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. Reinforcement learning and simulationbased search in computer go david silver ph.
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. Littman, with 2761 highly influential citations and 361 scientific research papers. Littman computational learning theory studies mathematical models that allow one to formally analyze and compare the performance of supervised learning algorithms such as. In proceedings of the eleventh international conference on machine learning, pages 157163, san francisco, ca, 1994.
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. 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. In advances in neural information processing systems, vol 2, 1990. Many algorithms for solving reinforcementlearning problems work by computing improved estimates of the optimal value function. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning of evaluation functions using temporal differencemonte carlo learning method.
Reinforcement learning is the problem of generating optimal behavior in a sequential decisionmaking environment given the opportunity of interacting with it. This tutorial will survey work in this area with an emphasis on recent results. It can then predict the outcome of its actions and make decisions that maximize its learning and task performance. Michael littman, computer science, rutgers initial explorations of cognitive reinforcement learning. Taylor and peter stone journal of machine learning research, volume 10, pp 16331685, 2009. Potentialbased shaping in modelbased reinforcement. However, the variance of the performance gradient estimates obtained from the simulation is sometimes excessive. Reinforcement learning via practice and critique advice. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Markov games as a framework for multiagent reinforcement learning. This paper surveys the field of reinforcement learning from a computerscience perspective. In machine learning, the problem of reinforcement learning is concerned with using experience gained through interacting with the world and evaluative feedback to improve a systems ability to make behavioral decisions.
It is written to be accessible to researchers familiar with machine learning. Lipschitz continuity in modelbased reinforcement learning. Convergence results for singlestep onpolicy reinforcementlearning algorithms by satinder singh, tommi jaakkola, michael littman, and csaba. Experiments with reinforcement learning in problems with continuous state and action spaces 1998 juan carlos santamaria, richard s. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Journal of articial in telligence researc h submitted published. Reinforcement learning improves behaviour from evaluative. It is available for download, but please send me mail if you try it out. Reinforcement learning midterms due daily show video cs 536. Provably efficient learning with typed parametric models. There are also many related courses whose material is available online. My rutgers students were members of the rutgers laboratory for reallife reinforcement learning or rl 3. Reinforcement learning for spoken dialogue systems by satinder singh, michael kearns, diane litman and marilyn walker.
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. Near optimal behavior via approximate state abstraction. Markov games as a framework for multiagent reinforcement learning michael l. Nearoptimal reinforcement learning in polynomial time satinder singh and michael kearns. You have been an academic in ai for more than 25 years during which time you mainly worked on reinforcement learning. Michael lederman littman born august 30, 1966 is a computer scientist.
The reinforcement learning rl problem is the challenge of artificial intelligence in a microcosm. Both the historical basis of the eld and a broad selection of current work are. Reinforcement learning is a subfield of machine learning, but is also a general purpose formalism for automated decisionmaking and ai. Journal of articial in telligence researc h submitted. In this thesis, i explore the relevance of computational reinforcement learning to the philosophy of rationality and concept formation. A survey, authorleslie pack kaelbling and michael l. Pdf reducing reinforcement learning to kwik online regression. 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. Proceedings of the eighteenth international conference on machine learning, pp. Rmax a general polynomial time algorithm for nearoptimal reinforcement learning. A unified analysis of valuefunctionbased reinforcement. Michael lederman littman, an american mathematician, computer scientist and professor of cs at brown university, and before at rutgers university and duke university. This paper surveys the eld of reinforcement learning from a computerscience per spective.
Also appeared in a special issue of the journal machine learning, 2002. Dissertation, university of alberta, edmonton, alberta, canada, 2009. 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. Transfer learning for reinforcement learning domains. Michael littman was born august 30th, 1966, in philadelphia, pennsylvania. Variance reduction techniques for gradient estimates in.