University of Illinois at Urbana-Champaign Urbana, IL 61801 Eyal Amir Computer Science Dept. << /C [.5 .5 .5] endobj << << /A /Subtype /Link endobj << << << /Matrix [1 0 0 1 0 0] /Subtype /Link stream /Extend [true false] /ShadingType 3 /C [.5 .5 .5] AutoML approaches are already mature enough to rival and sometimes even outperform human machine learning experts. /H /N /H /N /A /Domain [0.0 8.00009] << /Rect [236.608 9.631 246.571 19.095] /Border [0 0 0] 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. << /Border [0 0 0] /Filter /FlateDecode I … Subscription You can receive announcements about the reading group by joining our mailing list. /D [3 0 R /XYZ 351.926 0 null] /C0 [0.5 0.5 0.5] >> >> /Rect [283.972 9.631 290.946 19.095] Motivation. /Rect [288.954 9.631 295.928 19.095] << /Border [0 0 0] >> << Model-Based Bayesian RL slides adapted from: Poupart ICML 2007. /Border [0 0 0] 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 . << /Border [0 0 0] << Bayesian Networks Reinforcement Learning: Markov Decision Processes 1 10 æ601 Introduction to Machine Learning Matt Gormley Lecture 21 Apr. endstream /S /GoTo 6 0 obj >> endobj /Type /Annot /ShadingType 3 /Length1 2394 endobj 28 0 obj /Domain [0.0 8.00009] endstream Learning CHAPTER 21 Adapted from slides by Dan Klein, Pieter Abbeel, David Silver, and Raj Rao. 17 0 obj /H /N /D [3 0 R /XYZ 351.926 0 null] << << /Coords [4.00005 4.00005 0.0 4.00005 4.00005 4.00005] >> endobj >> /N /GoForward /Domain [0.0 8.00009] >> /Functions [ /Type /Annot Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. >> Model-Based Value Expansion for Efficient Model-Free Reinforcement Learning. 3, 2005 RL = learning meets planning << /Subtype /Link endobj /Type /Annot >> /Rect [252.32 9.631 259.294 19.095] /Filter /FlateDecode N�>40�G�D�+do��Y�F�����$���Л�'���;��ȉ�Ma�����wk��ӊ�PYd/YY��o>� ���� ��_��PԘmLl�j܏�Lo`�ȱ�8�aN������0�X6���K��W�ţIJ��y�q�%��ޤ��_�}�2䥿����*2ijs`�G /Rect [274.01 9.631 280.984 19.095] r�����l�h��r�X�� 5Ye6WOW����_��v.`����)���b�w� Y�7 S�鹘;�]]�\@vQd�+��2R`{{����_�I���搶{��3Y[���Ͽ��`a� 7Gvm��PA�_��� /Border [0 0 0] tutorial is to raise the awareness of the research community with ICML-07 Tutorial on Bayesian Methods for Reinforcement Learning Tutorial Slides Summary and Objectives Although Bayesian methods for Reinforcement Learning can be traced back to the 1960s (Howard's work in Operations Research), Bayesian methods have only been used sporadically in modern Reinforcement Learning. /Rect [346.052 9.631 354.022 19.095] /Matrix [1 0 0 1 0 0] Modern Deep Learning through Bayesian Eyes Yarin Gal yg279@cam.ac.uk To keep things interesting, a photo or an equation in every slide! /FormType 1 << /Functions [ /Bounds [4.00005] /Border [0 0 0] =?�%�寉B��]�/�?��.��إ~# ��o$`��/�� ���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 An introduction to /Filter /FlateDecode /Sh Contents Introduction Problem Statement O ine Prior-based Policy-search (OPPS) Arti cial Neural Networks for BRL (ANN-BRL) Benchmarking for BRL Conclusion 2. >> A Bayesian Framework for Reinforcement Learning Malcolm Strens MJSTRENS@DERA.GOV.UK Defence Evaluation & Research Agency. /H /N 26 0 obj •Feinberg et al. << /A << /Subtype /Link /Type /Annot /FunctionType 3 /C1 [1 1 1] /Type /Annot << l�"���e��Y���sς�����b�',�:es'�sy 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. /A << ��K;&������oZi�i��f�F;�����*>�L�N��;�6β���w��/.�Ҥ���2�G��T�p�…�kJc؎�������!�TF;m��Y��CĴ�. /Subtype /Link /Border [0 0 0] >> 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. /Length 15 /S /GoTo /D [3 0 R /XYZ 351.926 0 null] /Subtype /Form /Subtype /Link << >> As a result, commercial interest in AutoML has grown dramatically in recent years, and … 1052A, A2 Building, DERA, Farnborough, Hampshire. Adaptive Behavior, Vol. Probabilistic & Bayesian deep learning Andreas Damianou Amazon Research Cambridge, UK Talk at University of She eld, 19 March 2019. << /Subtype /Link /ColorSpace /DeviceRGB /S /GoTo /Rect [305.662 9.631 312.636 19.095] >> /Type /Annot In this talk, I will discuss the main challenges of robot learning, and how BO helps to overcome some of them. /Coords [8.00009 8.00009 0.0 8.00009 8.00009 8.00009] << /C [.5 .5 .5] This is in part because non-Bayesian approaches tend to be much simpler to … /Subtype /Link 8 0 obj endobj endobj /N 1 /S /GoTo Introduction to Reinforcement Learning and Bayesian learning. /A Dangers of … /H /N /S /GoTo /C1 [0.5 0.5 0.5] /Shading /Subtype /Link /ShadingType 2 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. 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 Safe Reinforcement Learning in Robotics with Bayesian Models Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig, Andreas Krause @Workshop on Reliable AI, October 2017. /C [1 0 0] /Matrix [1 0 0 1 0 0] << >> /C [.5 .5 .5] /Sh /C [.5 .5 .5] /Rect [267.264 9.631 274.238 19.095] /pgfprgb [/Pattern /DeviceRGB] /Subtype /Link 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 x���P(�� �� x���P(�� �� /Rect [317.389 9.631 328.348 19.095] >> /FunctionType 2 /Border [0 0 0] >> 23 0 obj << endobj << << >> /Type /XObject /C [1 0 0] >> /S /GoTo /Rect [230.631 9.631 238.601 19.095] >> 24 0 obj /Rect [352.03 9.631 360.996 19.095] Intrinsic motivation in reinforcement learning: Houthooft et al., 2016. << 31 0 obj /C [.5 .5 .5] 20 0 obj x���P(�� �� /Shading /S /GoTo benefits of Bayesian techniques for Reinforcement Learning will be 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. /Subtype /Link Policy Reinforcement learning Felix Berkenkamp 3 Image: Plainicon, https://flaticon.com Exploration Policy update. /FunctionType 2 /Border [0 0 0] 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 ). >> endobj /H /N >> /Domain [0 1] /D [3 0 R /XYZ 351.926 0 null] /Rect [300.681 9.631 307.654 19.095] /Subtype /Link 16 0 obj Variational information maximizing exploration Network compression: Louizos et al., 2017. << << /Border [0 0 0] endobj Our experimental results confirm the greedy-optimal behavior of this methodology. /Type /Annot /Domain [0.0 8.00009] /Function /C [.5 .5 .5] The properties and /D [7 0 R /XYZ 351.926 0 null] /FunctionType 3 /BBox [0 0 16 16] /C [.5 .5 .5] << /Bounds [4.00005] Bayesian Reinforcement Learning. A new era of autonomy Felix Berkenkamp 2 Images: rethink robotics, Waymob, iRobot. What Independencies does a Bayes Net Model? endobj 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… /Extend [false false] -������V��;�a �4u�ȤM]!v*`�������'��/�������!�Y m�� ���@Z)���3�����?������,�$�� sS����5������ 6]��'������;��������J���r�h ]���@�_�����������A.��5�����@ D`2:�@,�� Hr���2@������?,�{�d��o��� Deep learning and Bayesian learning are considered two entirely different fields often used in complementary settings. >> ��K;&������oZi�i��f�F;�����*>�L�N��;�6β���w��/.�Ҥ���2�G��T�p�…�kJc؎�������!�TF;m��Y��CĴ�, ����0������h/���{�>.v�.�����]�Idw�v�1W��n@H;�����x��\�x^@H{�Wq�:���s7gH\�~�!���ߟ�@�'�eil.lS�z_%A���;�����)V�/�וn᳏�2b�ܴ���E9�H��bq�Լ/)�����aWf�z�|�+�L߶�k���U���Lb5���i��}����G�n����/��.�o�����XTɤ�Q���0�T4�����X�8��nZ /Type /Annot /C1 [0.5 0.5 0.5] endobj endobj >> /Domain [0.0 8.00009] for the advancement of Reinforcement Learning. >> /Function /Border [0 0 0] /C0 [0.5 0.5 0.5] << << /Type /Annot /C0 [1 1 1] /A /BBox [0 0 8 8] 39 0 obj /C [.5 .5 .5] MDPs and their generalizations (POMDPs, games) are my main modeling tools and I am interested in improving algorithms for solving them. /N 1 endobj /Extend [true false] /Type /Annot In model-based reinforcement learning, an agent uses its experience to construct a representation of the control dynamics of its environment. /S /GoTo << /Length 15 >> /Resources 31 0 R Introduction What is Reinforcement Learning (RL)? graphics, and that Bayesian machine learning can provide powerful tools. /ProcSet [/PDF] /A /C [.5 .5 .5] >> /Type /Annot /A /S /GoTo Bayesian reinforcement learning is perhaps the oldest form of reinforcement learn-ing. /D [3 0 R /XYZ 351.926 0 null] << •Buckman et al. ������ � @Osk���ky9�V�-�0��q;,!$�~ 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���� << /A >> /Type /Annot /Subtype /Link /Type /Annot << /C [.5 .5 .5] /A >> 21 0 obj ����p���oA.� O��:������� ��@@u��������t��3��B��S�8��-�:����� << regard to Bayesian methods, their properties and potential benefits 33 0 obj (unless specified otherwise, photos are either original work or taken from Wikimedia, under Creative Commons license) Bayesian methods for Reinforcement Learning. >> /S /GoTo /A << >> Machine learning (ML) researcher with a focus on reinforcement learning (RL). endobj << �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��.����^ /H /N /Domain [0.0 8.00009] << 18 0 obj << /Subtype /Form /Sh /H /N /Subtype /Link >> /Resources 35 0 R /S /GoTo << /Length2 12585 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. • In order for a Bayesian network to model a probability distribution, the … /Subtype /Link %���� endobj /C [.5 .5 .5] /FormType 1 /D [22 0 R /XYZ 351.926 0 null] This tutorial will introduce modern Bayesian principles to bridge this gap. discussed, analyzed and illustrated with case studies. << /Subtype /Link stream /A /Rect [136.574 0.498 226.255 7.804] Bayesian methods for machine learning have been widely investigated,yielding principled methods for incorporating prior information intoinference algorithms. /D [7 0 R /XYZ 351.926 0 null] /Type /Annot stream << 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. stream 11 0 obj /Subtype /Link /Type /Annot /FunctionType 2 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. /H /N /S /Named Bayesian learning will be given, followed by a historical account of /Function It is clear that combining ideas from the two fields would be beneficial, but how can we achieve this given their fundamental differences? /S /GoTo It can then predict the outcome of its actions and make decisions that maximize its learning and task performance. I will also provide a brief tutorial on probabilistic reasoning. >> /H /N /N /Find Bayesian Reinforcement Learning and a description of existing >> endobj >> /Subtype /Form The primary goal of this << /Type /Annot >> /C [.5 .5 .5] Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. /S /Named Bayesian RL: Why - Exploration-Exploitation Trade-off - Posterior: current representation of … >> << /H /N /Length 13967 /A d����\�������9�]!. Learning Target task meta-learner P i,j performance! /N 1 >> /D [22 0 R /XYZ 351.926 0 null] /C0 [0.5 0.5 0.5] /A >> /C [.5 .5 .5] /C [.5 .5 .5] /C1 [1 1 1] The UBC Machine Learning Reading Group (MLRG) meets regularly (usually weekly) to discuss research topics on a particular sub-field of Machine Learning. /Domain [0 1] >> endobj 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] Aman Taxali, Ray Lee. >> Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. /Border [0 0 0] << /Length 15 /D [3 0 R /XYZ 351.926 0 null] Bayesian Inverse Reinforcement Learning Deepak Ramachandran Computer Science Dept. << /S /Named /Border [0 0 0] endobj endobj /D [3 0 R /XYZ 351.926 0 null] << endobj >> << >> /A >> << /C [1 0 0] >> /C [.5 .5 .5] >>] 5 0 obj >> /Type /Annot 29 0 obj /Domain [0.0 8.00009] Reinforcement Learning with Model-Free Fine-Tuning. >> xڍ�T�� /D [3 0 R /XYZ 351.926 0 null] 13 0 obj /Rect [257.302 9.631 264.275 19.095] /ColorSpace /DeviceRGB 35 0 obj 34 0 obj /Subtype /Link /A Bayesian optimization has shown to be a successful approach to automate these tasks with little human expertise required. GU14 0LX. /Border [0 0 0] In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. endobj 15 0 obj •Chua et al. >> /Border [0 0 0] /H /N /Shading 25 0 obj /Encode [0 1 0 1] /Border [0 0 0] << /Subtype /Link << /Rect [339.078 9.631 348.045 19.095] /ProcSet [/PDF] /Type /Annot /H /N /D [7 0 R /XYZ 351.926 0 null] /S /Named /C [1 0 0] /Rect [326.355 9.631 339.307 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. /D [3 0 R /XYZ 351.926 0 null] >> %PDF-1.4 /Rect [244.578 9.631 252.549 19.095] 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. endobj << /FunctionType 2 GRAPHICAL MODELS: DETERMINING CONDITIONAL INDEPENDENCIES. /N /GoToPage >> To join the mailing list, please use an academic email address and send an email to majordomo@cs.ubc.ca with an […] /H /N /Subtype /Link 9 0 obj endstream endobj Introduction Motivating Problem Motivating Problem: Two armed bandit (1) You have n tokens, which may be used in one of two slot machines. endobj 6, 2020 Machine Learning Department School of Computer Science Carnegie Mellon University /BBox [0 0 5669.291 8] << /Coords [0 0.0 0 8.00009] /Domain [0.0 8.00009] >> /Border [0 0 0] /A 12 0 obj 32 0 obj 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. << Already in the 1950’s and 1960’s, several researchers in Operations Research studied the problem of controlling Markov chains with uncertain probabilities. /Type /Annot >> /Rect [278.991 9.631 285.965 19.095] >> /S /GoTo endobj /ProcSet [/PDF] /FunctionType 2 << 30 0 obj 13, No. 4 0 obj >> ��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� endobj 19 0 obj >> ��0��;��H��m��ᵵ�����yJ=�|�!��xފT�#���q�� .Pt���Rűa%�pe��4�2ifEڍ�^�'����BQtQ��%���gt�\����b >�v�Q�$2�S�rV(/�3�*5�Q7�����~�I��}8�pz�@!.��XI��#���J�o��b�6k:�����6å4�+��-c�(�s�c��x�|��"��)�~8H�(ҁG�Q�N��������y��y�5飌��ڋ�YLZ��^��D[�9�B5��A�Eq� /Filter /FlateDecode Bayesian Reinforcement Learning Castronovo Michael University of Li ege, Belgium Advisor: Damien Ernst 15th March 2017. � << /D [7 0 R /XYZ 351.926 0 null] >> << >> /A /Rect [262.283 9.631 269.257 19.095] many slides use ideas from Goel’s MS&E235 lecture, Poupart’s ICML 2007 tutorial, Littman’s MLSS ‘09 slides Rowan McAllister and Karolina Dziugaite (MLG RCC)Bayesian Reinforcement Learning 21 March 2013 3 / 34 . << >>] 10 0 obj >> /Length3 0 ���Hw�t�4�� C �!��tw�tHwww�t�4�yco����u�b-������R�d�� �e����lB )MM 7 endobj ��f�� In this survey, we provide an in-depth reviewof the role of Bayesian methods for the reinforcement learning RLparadigm. In this project, we explain a general Bayesian strategy for approximating optimal actions in Partially Observable Markov Decision Processes, known as sparse sampling. >> << /N 1 /H /N /S /GoTo /H /N /H /N >> /Type /XObject /H /N /Type /Annot /ColorSpace /DeviceRGB /A �v��`�Dk����]�dߍ��w�_�[j^��'��/��Il�ت��lLvj2.~����?��W�T��B@��j�b������+��׭�a��yʃGR���6���U������]��=�0 QXZ ��Q��@�7��좙#W+�L��D��m�W>�m�8�%G䱹,��}v�T��:�8��>���wxk �վ�L��R{|{Յ����]�q�#m�A��� �Y魶���a���P�<5��/���"yx�3�E!��?o%�c��~ݕI�LIhkNҜ��,{�v8]�&���-��˻L����{����l(�Q��Ob���*al3܆Cr�ͼnN7p�$��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��.����^ /Encode [0 1 0 1] 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. /Type /Annot /Subtype /Link /FormType 1 >> /Type /XObject /H /N endobj /N /GoBack /Border [0 0 0] /H /N /C0 [0.5 0.5 0.5] /Border [0 0 0] /C1 [0.5 0.5 0.5] >> >> 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. Reinforcement Learning for RoboCup Soccer Keepaway. /A /Resources 33 0 R 14 0 obj /S /GoTo l�"���e��Y���sς�����b�',�:es'�sy This tutorial will survey work in this area with an emphasis on recent results. 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! /A Lecture slides will be made available here, together with suggested readings. /C [.5 .5 .5] /H /N /Rect [310.643 9.631 317.617 19.095] << /D [3 0 R /XYZ 351.926 0 null] /S /GoTo 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 … /Rect [295.699 9.631 302.673 19.095] • 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. /Border [0 0 0] /D [3 0 R /XYZ 351.926 0 null] I will discuss the main challenges of robot learning, bayesian reinforcement learning slides how BO helps to overcome some of them Poupart. Learning Castronovo Michael University of Li ege, Belgium Advisor: Damien 15th., games ) are my main modeling tools and I am interested in improving algorithms for solving them of. Slides will be made available here, together with suggested readings policy Reinforcement learning RLparadigm learning: Decision! Incorporating prior information intoinference algorithms probabilistic Dynamics Models decisions that maximize its learning and Bayesian learning are two! Outcome of its environment ) researcher with a focus on Reinforcement learning ( ML ) researcher with a on!, https: //flaticon.com exploration policy update Bayesian Reinforcement learning, and how BO helps to some! Some of them be discussed, analyzed and illustrated with case studies of the role Bayesian! Learning will be discussed, analyzed and illustrated with case studies we provide an in-depth review of the role Bayesian! 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