/Rect [283.972 9.631 290.946 19.095] /Type /Annot Bayesian methods for machine learning have been widely investigated,yielding principled methods for incorporating prior information intoinference algorithms. 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. /Shading /C [.5 .5 .5] Bayesian optimization has shown to be a successful approach to automate these tasks with little human expertise required. 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. << It is clear that combining ideas from the two fields would be beneficial, but how can we achieve this given their fundamental differences? >> /Border [0 0 0] /Subtype /Link /C [.5 .5 .5] /Sh /H /N 18 0 obj •Buckman et al. endobj ���Hw�t�4�� C �!��tw�tHwww�t�4�yco����u�b-������R�d�� �e����lB )MM 7 benefits of Bayesian techniques for Reinforcement Learning will be /Border [0 0 0] /Shading d����\�������9�]!. To join the mailing list, please use an academic email address and send an email to majordomo@cs.ubc.ca with an […] Bayesian Inverse Reinforcement Learning Deepak Ramachandran Computer Science Dept. << /Subtype /Link The UBC Machine Learning Reading Group (MLRG) meets regularly (usually weekly) to discuss research topics on a particular sub-field of Machine Learning. Bayesian learning will be given, followed by a historical account of << /S /Named Deep learning and Bayesian learning are considered two entirely different fields often used in complementary settings. /ProcSet [/PDF] 13, No. /Rect [300.681 9.631 307.654 19.095] /A >> /Length1 2394 xڍ�T�� 14 0 obj /Border [0 0 0] /C [.5 .5 .5] endobj GRAPHICAL MODELS: DETERMINING CONDITIONAL INDEPENDENCIES. << /Rect [295.699 9.631 302.673 19.095] << Bayesian RL: Why - Exploration-Exploitation Trade-off - Posterior: current representation of … /A /Functions [ As a result, commercial interest in AutoML has grown dramatically in recent years, and … << /Sh /Length2 12585 Reinforcement Learning qBasic idea: oReceive feedback in the form of rewards oAgent’s utility is defined by the reward function oMust (learn to) act so as to maximize expected rewards oAll learning is based on observed samples of outcomes! /Rect [136.574 0.498 226.255 7.804] /Rect [326.355 9.631 339.307 19.095] /Border [0 0 0] /H /N /Domain [0.0 8.00009] /FormType 1 /H /N /S /GoTo /C0 [0.5 0.5 0.5] Lecture slides will be made available here, together with suggested readings. Model-Based Value Expansion for Efficient Model-Free Reinforcement Learning. << 13 0 obj >> 26 0 obj << >> << 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 ). What Independencies does a Bayes Net Model? << /A << /Border [0 0 0] Our experimental results confirm the greedy-optimal behavior of this methodology. x���P(�� �� /H /N /H /N << /N /Find /D [3 0 R /XYZ 351.926 0 null] >> A new era of autonomy Felix Berkenkamp 2 Images: rethink robotics, Waymob, iRobot. 25 0 obj 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. >> /A /D [3 0 R /XYZ 351.926 0 null] /Type /Annot /S /Named >> ��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� /Extend [true false] /Type /XObject >>] /Coords [8.00009 8.00009 0.0 8.00009 8.00009 8.00009] /Border [0 0 0] /Domain [0.0 8.00009] /Domain [0.0 8.00009] /N /GoBack /Subtype /Form >> 5 0 obj for the advancement of Reinforcement Learning. 15 0 obj /Resources 31 0 R This tutorial will introduce modern Bayesian principles to bridge this gap. 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 … /S /GoTo /Subtype /Link /ProcSet [/PDF] /A /Border [0 0 0] /C [.5 .5 .5] Bayesian reinforcement learning is perhaps the oldest form of reinforcement learn-ing. /C1 [1 1 1] /C [.5 .5 .5] /Bounds [4.00005] /Type /Annot Intrinsic motivation in reinforcement learning: Houthooft et al., 2016. Learning Target task meta-learner P i,j performance! Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. /S /GoTo Introduction Motivating Problem Motivating Problem: Two armed bandit (1) You have n tokens, which may be used in one of two slot machines. << /S /GoTo stream >> /Border [0 0 0] /Type /Annot /Border [0 0 0] >> /Filter /FlateDecode >>] /C [.5 .5 .5] graphics, and that Bayesian machine learning can provide powerful tools. /Border [0 0 0] /Type /Annot Model-Based Bayesian RL slides adapted from: Poupart ICML 2007. /C1 [1 1 1] l�"���e��Y���sς�����b�',�:es'�sy 6, 2020 Machine Learning Department School of Computer Science Carnegie Mellon University >> (unless specified otherwise, photos are either original work or taken from Wikimedia, under Creative Commons license) 10 0 obj /FunctionType 2 %PDF-1.4 3, 2005 RL = learning meets planning << 21 0 obj /Subtype /Form /Subtype /Link /Type /Annot =?�%�寉B��]�/�?��.��إ~# ��o$`��/�� ���F� v�߈���A�)�F�|ʿ$��oɠ�_$ ɠ�A2���� ��$��o�`��� �t��!�L#?�����������t�-��������R��oIkr6w�����?b^Hs�d�����ey�~����[�!� G�0 �Ob���Nn����i��o1�� y!,A��������?������wŐ Z{9Z����@@Hcm���V���A���qu�l�zH����!���QC�w���s�|�9���x8�����x �t�����0������h/���{�>.v�.�����]�Idw�v�1W��n@H;�����x��\�x^@H{�Wq�:���s7gH\�~�!���ߟ�@�'�eil.lS�z_%A���;�����)V�/�וn᳏�2b�ܴ���E9�H��bq�Լ/)�����aWf�z�|�+�L߶�k���U���Lb5���i��}����G�n����/��.�o�����XTɤ�Q���0�T4�����X�8��nZ /N 1 /Border [0 0 0] /Subtype /Link >> /D [7 0 R /XYZ 351.926 0 null] ICML-07 Tutorial on Bayesian Methods for Reinforcement Learning Tutorial Slides Summary and Objectives Although Bayesian methods for Reinforcement Learning can be traced back to the 1960s (Howard's work in Operations Research), Bayesian methods have only been used sporadically in modern Reinforcement Learning. << /Rect [252.32 9.631 259.294 19.095] University of Illinois at Urbana-Champaign Urbana, IL 61801 Eyal Amir Computer Science Dept. >> >> >> Dangers of … >> AutoML approaches are already mature enough to rival and sometimes even outperform human machine learning experts. /Function /Border [0 0 0] /C0 [0.5 0.5 0.5] -������V��;�a �4u�ȤM]!v*`�������'��/�������!�Y m�� ���@Z)���3�����?������,�$�� sS����5������ 6]��'������;��������J���r�h ]���@�_�����������A.��5�����@ D`2:�@,�� Hr���2@������?,�{�d��o��� /ColorSpace /DeviceRGB /Rect [244.578 9.631 252.549 19.095] /N /GoForward << 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 [278.991 9.631 285.965 19.095] << stream /FunctionType 2 endobj /Coords [0 0.0 0 8.00009] >> /Type /Annot 9 0 obj In Bayesian learning, uncertainty is expressed by a prior distribution over unknown parameters and learning is achieved by computing a posterior distribution based on the data … The primary goal of this endobj r�����l�h��r�X�� 5Ye6WOW����_��v.`����)���b�w� Y�7 S�鹘;�]]�\@vQd�+��2R`{{����_�I���搶{��3Y[���Ͽ��`a� 7Gvm��PA�_��� << 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] /FunctionType 2 /H /N Bayesian Reinforcement Learning Castronovo Michael University of Li ege, Belgium Advisor: Damien Ernst 15th March 2017. >> endobj /S /GoTo << << /Type /Annot /Length 15 /Domain [0.0 8.00009] /Filter /FlateDecode ����p���oA.� O��:������� ��@@u��������t��3��B��S�8��-�:����� endobj << MDPs and their generalizations (POMDPs, games) are my main modeling tools and I am interested in improving algorithms for solving them. /D [3 0 R /XYZ 351.926 0 null] /Border [0 0 0] >> >> Motivation. /Rect [352.03 9.631 360.996 19.095] ��f�� 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. endobj << >> I will also provide a brief tutorial on probabilistic reasoning. << >> /D [22 0 R /XYZ 351.926 0 null] /H /N endobj /D [7 0 R /XYZ 351.926 0 null] In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. /ShadingType 3 Bayesian Networks Reinforcement Learning: Markov Decision Processes 1 10 æ601 Introduction to Machine Learning Matt Gormley Lecture 21 Apr. 31 0 obj Variational information maximizing exploration Network compression: Louizos et al., 2017. /S /GoTo endobj >> endobj /ShadingType 3 /C0 [0.5 0.5 0.5] 23 0 obj /Rect [305.662 9.631 312.636 19.095] /H /N endobj /D [7 0 R /XYZ 351.926 0 null] /C1 [0.5 0.5 0.5] Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. << 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 . >> /Type /Annot /C [.5 .5 .5] /A << 30 0 obj 29 0 obj /S /GoTo << endobj Machine learning (ML) researcher with a focus on reinforcement learning (RL). /H /N •Chua et al. /Subtype /Link /C [1 0 0] >> /Rect [262.283 9.631 269.257 19.095] Bayesian methods for Reinforcement Learning. GU14 0LX. /C [1 0 0] << /Rect [339.078 9.631 348.045 19.095] 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. /A /Subtype /Link /Border [0 0 0] /C [.5 .5 .5] /D [7 0 R /XYZ 351.926 0 null] /BBox [0 0 16 16] /Domain [0 1] >> /Subtype /Form 35 0 obj Put simply, AutoML can lead to improved performance while saving substantial amounts of time and money, as machine learning experts are both hard to find and expensive. >> In this talk, I will discuss the main challenges of robot learning, and how BO helps to overcome some of them. << 33 0 obj /H /N >> /S /GoTo /Filter /FlateDecode /Type /Annot /H /N >> << endobj /A >> /S /GoTo << endobj /S /GoTo ��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 /Type /Annot /Rect [236.608 9.631 246.571 19.095] Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. /Type /Annot << ������ � @Osk���ky9�V�-�0��q;,!$�~ K �����;������S���`2w��@(��C�@�0d�� O�d�8}���w��� ;�y�6�{��zjZ2���0��NR� �a���r�r 89�� �|� �� ������RuSп�q����` ��Ҽ��p�w-�=F��fPCv`������o����o��{�W������ɺ����f�[���6��y�k Ye7W�Y��!���Mu���� N�>40�G�D�+do��Y�F�����$���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 /A >> << /S /Named /Subtype /Link /Type /Annot endobj /C [.5 .5 .5] /pgfprgb [/Pattern /DeviceRGB] /S /Named /Rect [267.264 9.631 274.238 19.095] 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. /ShadingType 2 endobj /Subtype /Link In this survey, we provide an in-depth reviewof the role of Bayesian methods for the reinforcement learning RLparadigm. I … /S /GoTo /Subtype /Link 6 0 obj Aman Taxali, Ray Lee. << �v��`�Dk����]�dߍ��w�_�[j^��'��/��Il�ت��lLvj2.~����?��W�T��B@��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�$��k�Y�Ҧ�r}b�7��T��vC�b��0�DO��h����+=z/'i�\2*�Lʈ�`�?��L_��dm����nTn�s�-b��[����=����V��"w�(ע�e�����*X�I=X���s CJ��ɸ��4lm�;%�P�Zg��.����^ >> /Type /Annot discussed, analyzed and illustrated with case studies. 28 0 obj /C1 [0.5 0.5 0.5] /Length3 0 Subscription You can receive announcements about the reading group by joining our mailing list. N�>40�G�D�+do��Y�F�����$���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 /Subtype /Link /Filter /FlateDecode << 24 0 obj /S /GoTo This is in part because non-Bayesian approaches tend to be much simpler to … Modern Deep Learning through Bayesian Eyes Yarin Gal yg279@cam.ac.uk To keep things interesting, a photo or an equation in every slide! endobj 32 0 obj tutorial is to raise the awareness of the research community with /Domain [0.0 8.00009] Introduction to Reinforcement Learning and Bayesian learning. ��K;&������oZi�i��f�F;�����*>�L�N��;�6β���w��/.�Ҥ���2�G��T�p�…�kJc؎�������!�TF;m��Y��CĴ�. /Length 15 11 0 obj << /ProcSet [/PDF] /D [3 0 R /XYZ 351.926 0 null] >> /Subtype /Link /Rect [317.389 9.631 328.348 19.095] /ColorSpace /DeviceRGB /Rect [257.302 9.631 264.275 19.095] /Type /XObject << /Rect [230.631 9.631 238.601 19.095] /Rect [310.643 9.631 317.617 19.095] /Bounds [4.00005] stream /A << endobj >> endobj • 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. •Feinberg et al. << /Domain [0.0 8.00009] 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. /Matrix [1 0 0 1 0 0] /S /GoTo << 34 0 obj /BBox [0 0 5669.291 8] /BBox [0 0 8 8] >> << /FunctionType 3 >> /D [3 0 R /XYZ 351.926 0 null] Reinforcement Learning with Model-Free Fine-Tuning. /FormType 1 << Already in the 1950’s and 1960’s, several researchers in Operations Research studied the problem of controlling Markov chains with uncertain probabilities. /S /GoTo /C [.5 .5 .5] /N /GoToPage /Subtype /Link /H /N /Domain [0.0 8.00009] regard to Bayesian methods, their properties and potential benefits /Matrix [1 0 0 1 0 0] >> 39 0 obj >> /Rect [274.01 9.631 280.984 19.095] /Length 15 /A 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. 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. << 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… endobj /Resources 33 0 R /H /N >> /H /N /C [.5 .5 .5] Learning CHAPTER 21 Adapted from slides by Dan Klein, Pieter Abbeel, David Silver, and Raj Rao. /Encode [0 1 0 1] /Shading /A /Type /Annot /Coords [4.00005 4.00005 0.0 4.00005 4.00005 4.00005] /Extend [true false] 16 0 obj /C [.5 .5 .5] endobj /Rect [288.954 9.631 295.928 19.095] /N 1 Probabilistic & Bayesian deep learning Andreas Damianou Amazon Research Cambridge, UK Talk at University of She eld, 19 March 2019. /Rect [346.052 9.631 354.022 19.095] /N 1 endobj Bayesian Reinforcement Learning: A Survey Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar Presented by Jacob Nogas ft. Animesh Garg (cameo) Bayesian RL: What - Leverage Bayesian Information in RL problem - Dynamics - Solution space (Policy Class) - Prior comes from System Designer. /Border [0 0 0] It can then predict the outcome of its actions and make decisions that maximize its learning and task performance. << endobj 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. << /D [3 0 R /XYZ 351.926 0 null] l�"���e��Y���sς�����b�',�:es'�sy << /D [3 0 R /XYZ 351.926 0 null] � >> /A 12 0 obj /C [1 0 0] 1052A, A2 Building, DERA, Farnborough, Hampshire. /H /N << >> /C [.5 .5 .5] /D [22 0 R /XYZ 351.926 0 null] x���P(�� �� /H /N /Domain [0.0 8.00009] >> 17 0 obj /Type /Annot >> /N 1 >> /Border [0 0 0] /D [3 0 R /XYZ 351.926 0 null] /Subtype /Link /Subtype /Link /C1 [0.5 0.5 0.5] endstream
2020 bayesian reinforcement learning slides