endobj /Border [0 0 0] /Filter /FlateDecode History • Reinforcement Learning in AI: –Formalized in the 1980’s by Sutton, Barto and others –Traditional RL algorithms are not Bayesian • RL is the problem of controlling a Markov Chain with unknown probabilities. << /Rect [244.578 9.631 252.549 19.095] << endobj /Filter /FlateDecode I … benefits of Bayesian techniques for Reinforcement Learning will be 24 0 obj /Functions [ 34 0 obj << •Feinberg et al. /C [.5 .5 .5] In this project, we explain a general Bayesian strategy for approximating optimal actions in Partially Observable Markov Decision Processes, known as sparse sampling. /H /N << endobj l�"���e��Y���sς�����b�',�:es'�sy << •Buckman et al. /C [.5 .5 .5] Motivation. /H /N /S /GoTo x���P(�� �� endobj << endstream /Subtype /Link /FunctionType 3 /Rect [339.078 9.631 348.045 19.095] /Rect [305.662 9.631 312.636 19.095] /Domain [0.0 8.00009] What Independencies does a Bayes Net Model? << /D [3 0 R /XYZ 351.926 0 null] many slides use ideas from Goel’s MS&E235 lecture, Poupart’s ICML 2007 tutorial, Littman’s MLSS ‘09 slides Rowan McAllister and Karolina Dziugaite (MLG RCC)Bayesian Reinforcement Learning 21 March 2013 3 / 34 . >> >> endobj /Functions [ /Resources 33 0 R /A r�����l�h��r�X�� 5Ye6WOW����_��v.`����)���b�w� Y�7 S�鹘;�]]�\@vQd�+��2R`{{����_�I���搶{��3Y[���Ͽ��`a� 7Gvm��PA�_��� ������ � @Osk���ky9�V�-�0��q;,!$�~ K �����;������S���`2w��@(��C�@�0d�� O�d�8}���w��� ;�y�6�{��zjZ2���0��NR� �a���r�r 89�� �|� �� ������RuSп�q����` ��Ҽ��p�w-�=F��fPCv`������o����o��{�W������ɺ����f�[���6��y�k Ye7W�Y��!���Mu���� /BBox [0 0 8 8] Bayesian Reinforcement Learning Nikos Vlassis, Mohammad Ghavamzadeh, Shie Mannor, and Pascal Poupart AbstractThis chapter surveys recent lines of work that use Bayesian techniques for reinforcement learning. /Rect [274.01 9.631 280.984 19.095] << 1052A, A2 Building, DERA, Farnborough, Hampshire. /Resources 31 0 R /A 23 0 obj >> << /D [3 0 R /XYZ 351.926 0 null] The UBC Machine Learning Reading Group (MLRG) meets regularly (usually weekly) to discuss research topics on a particular sub-field of Machine Learning. /C0 [0.5 0.5 0.5] 18 0 obj >> << /C [.5 .5 .5] << endobj /Border [0 0 0] endobj /Length 15 /ShadingType 2 Bayesian reinforcement learning is perhaps the oldest form of reinforcement learn-ing. Reinforcement Learning Logistics and scheduling Acrobatic helicopters Load balancing Robot soccer Bipedal locomotion Dialogue systems Game playing Power grid control … Model: Peter Stone, Richard Sutton, Gregory Kuhlmann. Lecture slides will be made available here, together with suggested readings. Videolecture by Yee Whye Teh, with slides ; Videolecture by Michael Jordan, with slides Second part of ... Model-based Bayesian Reinforcement Learning in Partially Observable Domains (model based bayesian rl for POMDPs ) Pascal Poupart and Nikos Vlassis. /N 1 /Border [0 0 0] endobj >> University of Illinois at Urbana-Champaign Urbana, IL 61801 Eyal Amir Computer Science Dept. Reinforcement Learning vs Bayesian approach As part of the Computational Psychiatry summer (pre) course, I have discussed the differences in the approaches characterising Reinforcement learning (RL) and Bayesian models (see slides 22 onward, here: Fiore_Introduction_Copm_Psyc_July2019 ). /Border [0 0 0] /Subtype /Link /FunctionType 2 To join the mailing list, please use an academic email address and send an email to [email protected] with an […] >> /H /N stream /H /N GU14 0LX. /D [3 0 R /XYZ 351.926 0 null] << /Shading Learning Target task meta-learner P i,j performance! /Encode [0 1 0 1] /Type /Annot >> /Coords [0 0.0 0 8.00009] /C [.5 .5 .5] for the advancement of Reinforcement Learning. << << >> x���P(�� �� I will also provide a brief tutorial on probabilistic reasoning. /Domain [0 1] /Type /XObject >> /Rect [262.283 9.631 269.257 19.095] /BBox [0 0 5669.291 8] /Domain [0 1] << >> >> /FunctionType 2 /H /N 19 0 obj >> /S /GoTo << /Sh >> /Extend [true false] In model-based reinforcement learning, an agent uses its experience to construct a representation of the control dynamics of its environment. Bayesian Networks + Reinforcement Learning 1 10-601 Introduction to Machine Learning Matt Gormley Lecture 22 Nov. 14, 2018 Machine Learning Department School of Computer Science Carnegie Mellon University. /Type /XObject /Coords [4.00005 4.00005 0.0 4.00005 4.00005 4.00005] /Type /Annot >> /D [3 0 R /XYZ 351.926 0 null] >> /Subtype /Link A new era of autonomy Felix Berkenkamp 2 Images: rethink robotics, Waymob, iRobot. << /Rect [295.699 9.631 302.673 19.095] << Bayesian methods for Reinforcement Learning. /Type /Annot >> >> /Length 13967 /FormType 1 /N 1 /Subtype /Form Bayesian Inverse Reinforcement Learning Deepak Ramachandran Computer Science Dept. >> 6, 2020 Machine Learning Department School of Computer Science Carnegie Mellon University /D [3 0 R /XYZ 351.926 0 null] /C1 [1 1 1] /S /Named endobj /Type /Annot >> /N /GoBack /H /N /Subtype /Link endobj /ColorSpace /DeviceRGB >> /Sh >> Probabilistic & Bayesian deep learning Andreas Damianou Amazon Research Cambridge, UK Talk at University of She eld, 19 March 2019. /Matrix [1 0 0 1 0 0] /Type /XObject /Border [0 0 0] • In order for a Bayesian network to model a probability distribution, the … >> /Rect [257.302 9.631 264.275 19.095] This tutorial will introduce modern Bayesian principles to bridge this gap. /Function >> /S /GoTo ModelsModels Models • Select source tasks, transfer trained models to similar target task 1 • Use as starting point for tuning, or freeze certain aspects (e.g. /Type /Annot << /Type /Annot regard to Bayesian methods, their properties and potential benefits As a result, commercial interest in AutoML has grown dramatically in recent years, and … This time: Fast Learning (Bayesian bandits to MDPs) Next time: Fast Learning Emma Brunskill (CS234 Reinforcement Learning )Lecture 12: Fast Reinforcement Learning 1 Winter 2019 2 / 61. << /Type /Annot /FunctionType 2 A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning arXiv preprint arXiv:1012.2599, 2010; Shahriari, B.; Swersky, K.; Wang, Z.; Adams, R. P. & de Freitas, N. Taking the human out of the loop: A review of Bayesian … graphics, and that Bayesian machine learning can provide powerful tools. /Subtype /Link /Rect [300.681 9.631 307.654 19.095] endobj endstream >> /Sh /A Bayesian Reinforcement Learning and a description of existing /Length1 2394 26 0 obj /A /Subtype /Link endobj Model-Based Value Expansion for Efficient Model-Free Reinforcement Learning. << endobj endobj AutoML approaches are already mature enough to rival and sometimes even outperform human machine learning experts. Reinforcement learning is an area of machine learning in computer science, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. /Shading /Coords [8.00009 8.00009 0.0 8.00009 8.00009 8.00009] Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. /S /GoTo /C [.5 .5 .5] >> >> /ColorSpace /DeviceRGB /Border [0 0 0] /C0 [0.5 0.5 0.5] Contents Introduction Problem Statement O ine Prior-based Policy-search (OPPS) Arti cial Neural Networks for BRL (ANN-BRL) Benchmarking for BRL Conclusion 2. endobj /N /GoToPage /S /GoTo << << >> /Border [0 0 0] Safe Reinforcement Learning in Robotics with Bayesian Models Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig, Andreas Krause @Workshop on Reliable AI, October 2017. /Subtype /Link /D [3 0 R /XYZ 351.926 0 null] 13 0 obj 20 0 obj /H /N Intrinsic motivation in reinforcement learning: Houthooft et al., 2016. /Length 15 /S /GoTo I will attempt to address some of the common concerns of this approach, and discuss the pros and cons of Bayesian modeling, and brieﬂy discuss the relation to non-Bayesian machine learning. /Filter /FlateDecode /Subtype /Link /D [3 0 R /XYZ 351.926 0 null] endstream /Type /Annot >> /H /N In this talk, we show how the uncertainty information in Bayesian models can be used to make safe and informed decisions both in policy search and model-based reinforcement learning… /Type /Annot /FormType 1 /A /H /N MDPs and their generalizations (POMDPs, games) are my main modeling tools and I am interested in improving algorithms for solving them. This is in part because non-Bayesian approaches tend to be much simpler to … /Subtype /Link /Rect [136.574 0.498 226.255 7.804] /H /N /Border [0 0 0] Bayesian RL: Why - Exploration-Exploitation Trade-off - Posterior: current representation of … >> << >> 35 0 obj /Border [0 0 0] >> endobj /C0 [0.5 0.5 0.5] /Subtype /Link CS234 Reinforcement Learning Winter 2019 1With a few slides derived from David Silver Emma Brunskill (CS234 Reinforcement ... Fast Reinforcement Learning 1 Winter 2019 1 / 36. stream << << endobj /N 1 /A >> /A • Operations Research: Bayesian Reinforcement Learning already studied under the names of – Adaptive control processes [Bellman] – Dual control [Fel’Dbaum] – Optimal learning • 1950’s & 1960’s: Bellman, Fel’Dbaum, Howard and others develop Bayesian techniques to control Markov chains with uncertain probabilities and rewards. ��0��;��H��m��ᵵ�����yJ=�|�!��xފT�#���q�� .Pt���Rűa%�pe��4�2ifEڍ�^�'����BQtQ��%���gt�\����b >�v�Q�$2�S�rV(/�3�*5�Q7�����~�I��}8�pz�@!.��XI��#���J�o��b�6k:�����6å4�+��-c�(�s�c��x�|��"��)�~8H�(ҁG�Q�N��������y��y�5飌��ڋ�YLZ��^��D[�9�B5��A�Eq� /H /N /C [.5 .5 .5] /C0 [1 1 1] /S /GoTo >> 17 0 obj /ProcSet [/PDF] Policy Reinforcement learning Felix Berkenkamp 3 Image: Plainicon, https://flaticon.com Exploration Policy update. /H /N 29 0 obj /Domain [0.0 8.00009] << -������V��;�a �4u�ȤM]!v*`�������'��/�������!�Y m�� ���@Z)���3�����?������,�$�� sS����5������ 6]��'������;��������J���r�h ]���@�_�����������A.��5�����@ D`2:�@,�� Hr���[email protected]������?,�{�d��o��� /A << /Type /Annot /A /A /Border [0 0 0] Variational information maximizing exploration Network compression: Louizos et al., 2017. << Learning CHAPTER 21 Adapted from slides by Dan Klein, Pieter Abbeel, David Silver, and Raj Rao. 13, No. /H /N << /S /GoTo /H /N /Length3 0 /D [3 0 R /XYZ 351.926 0 null] << ��K;&������oZi�i��f�F;�����*>�L�N��;�6β���w��/.�Ҥ���2�G��T�p� �kJc؎�������!�TF;m��Y��CĴ�, ����0������h/���{�>.v�.�����]�Idw�v�1W��n@H;�����x��\�x^@H{�Wq�:���s7gH\�~�!���ߟ�@�'�eil.lS�z_%A���;�����)V�/�וn�2b�ܴ���E9�H��bq�Լ/)�����aWf�z�|�+�L߶�k���U���Lb5���i��}����G�n����/��.�o�����XTɤ�Q���0�T4�����X�8��nZ /FormType 1 21 0 obj Dangers of … /Type /Annot /Subtype /Link /ColorSpace /DeviceRGB << /H /N /Border [0 0 0] /S /GoTo /C [1 0 0] << /Type /Annot >> 37 0 obj [619.8 569.5 569.5 864.6 864.6 253.5 283 531.3 531.3 531.3 531.3 531.3 708.3 472.2 510.4 767.4 826.4 531.3 914.9 1033 826.4 253.5 336.8 531.3 885.4 531.3 885.4 805.6 295.1 413.2 413.2 531.3 826.4 295.1 354.2 295.1 531.3 531.3 531.3 531.3 531.3 531.3 531.3 531.3 531.3 531.3 531.3 295.1 295.1 336.8 826.4 501.7 501.7 708.3 708.3 708.3 678.8 767.4 637.2 607.6 708.3 750 295.1 501.7 737.9 578.1 927.1 750 784.7 678.8 784.7 687.5 590.3 725.7 729.2 708.3 1003.5 708.3 708.3 649.3 309 531.3 309 531.3 295.1 295.1 510.4 548.6 472.2 548.6 472.2 324.7 531.3 548.6 253.5 283 519.1 253.5 843.8 548.6 531.3 548.6 548.6 362.9 407.3 383.7 548.6 489.6 725.7 489.6 489.6 461.8] << /Subtype /Link /S /GoTo << /H /N /A ICML-07 Tutorial on Bayesian Methods for Reinforcement Learning Tutorial Slides Summary and Objectives Although Bayesian methods for Reinforcement Learning can be traced back to the 1960s (Howard's work in Operations Research), Bayesian methods have only been used sporadically in modern Reinforcement Learning. 5 0 obj /C [.5 .5 .5] endobj /H /N >> /Domain [0.0 8.00009] /Subtype /Link >> Bayesian optimization has shown to be a successful approach to automate these tasks with little human expertise required. /C1 [0.5 0.5 0.5] stream /C [.5 .5 .5] •Chua et al. << /Subtype /Link /Rect [352.03 9.631 360.996 19.095] /D [7 0 R /XYZ 351.926 0 null] << Bayesian Networks Reinforcement Learning: Markov Decision Processes 1 10 æ601 Introduction to Machine Learning Matt Gormley Lecture 21 Apr. /Border [0 0 0] /Domain [0.0 8.00009] << >> /Bounds [4.00005] Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth reviewof the role of Bayesian methods for the reinforcement learning RLparadigm. /Rect [326.355 9.631 339.307 19.095] /Subtype /Form << 8 0 obj /A /D [3 0 R /XYZ 351.926 0 null] /Subtype /Link >> /Border [0 0 0] /Filter /FlateDecode /D [22 0 R /XYZ 351.926 0 null] >> /ShadingType 3 10 0 obj /S /GoTo d����\�������9�]!. /Domain [0.0 8.00009] This tutorial will survey work in this area with an emphasis on recent results. /S /GoTo >> Bayesian learning will be given, followed by a historical account of /Border [0 0 0] Adaptive Behavior, Vol. /C [.5 .5 .5] 15 0 obj It can then predict the outcome of its actions and make decisions that maximize its learning and task performance. endobj /Border [0 0 0] /Extend [false false] /C [.5 .5 .5] /FunctionType 2 Already in the 1950’s and 1960’s, several researchers in Operations Research studied the problem of controlling Markov chains with uncertain probabilities. Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. /C [.5 .5 .5] >> << /C [.5 .5 .5] /A �v��`�Dk����]�dߍ��w�_�[j^��'��/��Il�ت��lLvj2.~����?��W�T��[email protected]��j�b������+���a��yʃGR���6���U������]��=�0 QXZ ��Q��@�7��좙#W+�L��D��m�W>�m�8�%G䱹,��}v�T��:�8��>���wxk �վ�L��R{|{Յ����]�q�#m�A��� �Y魶���a���P�<5��/���"yx�3�E!��?o%�c��~ݕI�LIhkNҜ��,{�v8]�&���-��˻L����{����l(�Q��Ob���*al3܆Cr�ͼnN7p�$��k�Y�Ҧ�r}b�7��T��vC�b��0�DO��h����+=z/'i�\2*�Lʈ�`�?��L_��dm����nTn�s�-b��[����=����V��"w�(ע�e�����*X�I=X���s CJ��ɸ��4lm�;%�P�Zg��.����^ tutorial is to raise the awareness of the research community with /D [3 0 R /XYZ 351.926 0 null] << >> %���� endobj /FunctionType 3 >> Introduction Motivating Problem Motivating Problem: Two armed bandit (1) You have n tokens, which may be used in one of two slot machines. endobj /Type /Annot /D [3 0 R /XYZ 351.926 0 null] /C1 [1 1 1] /Subtype /Link 12 0 obj << /N 1 /S /GoTo >> /Type /Annot /Bounds [4.00005] /A /S /GoTo /D [7 0 R /XYZ 351.926 0 null] >> The primary goal of this GRAPHICAL MODELS: DETERMINING CONDITIONAL INDEPENDENCIES. Machine learning (ML) researcher with a focus on reinforcement learning (RL). /Rect [278.991 9.631 285.965 19.095] 39 0 obj x���P(�� �� /C1 [0.5 0.5 0.5] /pgfprgb [/Pattern /DeviceRGB] In particular, I believe that finding the right ways to quantify uncertainty in complex deep RL models is one of the most promising approaches to improving sample-efficiency. ����p���oA.� O��:������� ��@@u��������t��3��B��S�8��-�:����� Bayesian Reinforcement Learning. A Bayesian Framework for Reinforcement Learning Malcolm Strens [email protected] Defence Evaluation & Research Agency. endobj << << 30 0 obj /S /GoTo /A /A endobj stream >> /Border [0 0 0] /N /GoForward Introduction What is Reinforcement Learning (RL)? >> /Border [0 0 0] /Rect [310.643 9.631 317.617 19.095] N�>40�G�D�+do��Y�F�����$���Л�'���;��ȉ�Ma�����wk��ӊ�PYd/YY��o>� ���� ��_��PԘmLl�j�Lo`�ȱ�8�aN������0�X6���K��W�ţĲ��y�q�%��ޤ��_�}�2䥿����*2ĳs`�G endobj /C0 [0.5 0.5 0.5] >> 11 0 obj /BBox [0 0 16 16] >>] /Length 15 4 0 obj Bayesian methods for machine learning have been widely investigated,yielding principled methods for incorporating prior information intoinference algorithms. /C [.5 .5 .5] Bayesian compression for deep learning Lots more references in CSC2541, \Scalable and Flexible Models of Uncertainty" https://csc2541-f17.github.io/ Roger Grosse and Jimmy Ba CSC421/2516 Lecture 19: Bayesian Neural Nets 22/22 . >> l�"���e��Y���sς�����b�',�:es'�sy /Type /Annot /Encode [0 1 0 1] /A 33 0 obj ��K;&������oZi�i��f�F;�����*>�L�N��;�6β���w��/.�Ҥ���2�G��T�p� �kJc؎�������!�TF;m��Y��CĴ�. Modern Deep Learning through Bayesian Eyes Yarin Gal [email protected] To keep things interesting, a photo or an equation in every slide!

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