bayesian reinforcement learning slides

13, No. << << >> >> It is clear that combining ideas from the two fields would be beneficial, but how can we achieve this given their fundamental differences? /C [1 0 0] To join the mailing list, please use an academic email address and send an email to [email protected] with an […] 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 this talk, I will discuss the main challenges of robot learning, and how BO helps to overcome some of them. /FunctionType 3 /A /FunctionType 2 >> << << endobj /Border [0 0 0] This tutorial will introduce modern Bayesian principles to bridge this gap. /C1 [0.5 0.5 0.5] Introduction to Reinforcement Learning and Bayesian learning. /Resources 31 0 R /H /N /A /Subtype /Link << Variational information maximizing exploration Network compression: Louizos et al., 2017. Probabilistic & Bayesian deep learning Andreas Damianou Amazon Research Cambridge, UK Talk at University of She eld, 19 March 2019. >> /Filter /FlateDecode /C1 [0.5 0.5 0.5] 32 0 obj >> /Border [0 0 0] 25 0 obj endobj /C [.5 .5 .5] /Subtype /Link /Sh /Border [0 0 0] Bayesian Reinforcement Learning and a description of existing /Subtype /Form /N /Find << << /Subtype /Link /Domain [0.0 8.00009] /Subtype /Form /S /GoTo /H /N Introduction What is Reinforcement Learning (RL)? /Rect [339.078 9.631 348.045 19.095] /D [3 0 R /XYZ 351.926 0 null] /Rect [244.578 9.631 252.549 19.095] << << Lecture slides will be made available here, together with suggested readings. /FunctionType 2 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. /C [.5 .5 .5] /S /GoTo /D [3 0 R /XYZ 351.926 0 null] /Domain [0.0 8.00009] /D [7 0 R /XYZ 351.926 0 null] /Filter /FlateDecode 39 0 obj /H /N stream >> /Coords [4.00005 4.00005 0.0 4.00005 4.00005 4.00005] >> << /C [.5 .5 .5] /A /Length 15 << 28 0 obj >> /S /GoTo 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] /Rect [317.389 9.631 328.348 19.095] Contents Introduction Problem Statement O ine Prior-based Policy-search (OPPS) Arti cial Neural Networks for BRL (ANN-BRL) Benchmarking for BRL Conclusion 2. /H /N /H /N /A l�"���e��Y���sς�����b�',�:es'�sy 1052A, A2 Building, DERA, Farnborough, Hampshire. << << << /Resources 35 0 R /A /S /GoTo � 6 0 obj << /C [.5 .5 .5] << /Encode [0 1 0 1] Reinforcement Learning with Model-Free Fine-Tuning. /Resources 33 0 R I … << /Subtype /Link /Subtype /Link /Rect [262.283 9.631 269.257 19.095] >> /Length 13967 /Subtype /Link << 13 0 obj /A >> /A GRAPHICAL MODELS: DETERMINING CONDITIONAL INDEPENDENCIES. /C [1 0 0] /FormType 1 /D [3 0 R /XYZ 351.926 0 null] << >> >> /H /N endobj /S /GoTo << Subscription You can receive announcements about the reading group by joining our mailing list. /ProcSet [/PDF] /Subtype /Link /FunctionType 2 /D [3 0 R /XYZ 351.926 0 null] /FunctionType 2 Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. endobj endobj /N 1 /Type /Annot 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. 34 0 obj In model-based reinforcement learning, an agent uses its experience to construct a representation of the control dynamics of its environment. /H /N 29 0 obj /A •Buckman et al. 20 0 obj 16 0 obj >> /A /H /N -������V��;�a �4u�ȤM]!v*`�������'��/�������!�Y m�� ���@Z)���3�����?������,�$�� sS����5������ 6]��'������;��������J���r�h ]���@�_�����������A.��5�����@ D`2:�@,�� Hr���[email protected]������?,�{�d��o��� 9 0 obj >> >> /Type /Annot >> >> Bayesian RL: Why - Exploration-Exploitation Trade-off - Posterior: current representation of … /Domain [0 1] >> 23 0 obj endobj /D [3 0 R /XYZ 351.926 0 null] graphics, and that Bayesian machine learning can provide powerful tools. << /Function ��K;&������oZi�i��f�F;�����*>�L�N��;�6β���w��/.�Ҥ���2�G��T�p�…�kJc؎�������!�TF;m��Y��CĴ�. �v��`�Dk����]�dߍ��w�_�[j^��'��/��Il�ت��lLvj2.~����?��W�T��[email protected]��j�b������+��׭�a��yʃGR���6���U������]��=�0 QXZ ��Q��@�7��좙#W+�L��D��m�W>�m�8�%G䱹,��}v�T��:�8��>���wxk �վ�L��R{|{Յ����]�q�#m�A��� �Y魶���a���P�<5��/���"yx�3�E!��?o%�c��~ݕI�LIhkNҜ��,{�v8]�&���-��˻L����{����l(�Q��Ob���*al3܆Cr�ͼnN7p�$��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��.����^ Model-Based Bayesian RL slides adapted from: Poupart ICML 2007. /C [.5 .5 .5] /Subtype /Link /Subtype /Link /S /GoTo /D [7 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 /Rect [257.302 9.631 264.275 19.095] /S /Named It can then predict the outcome of its actions and make decisions that maximize its learning and task performance. /A endobj << /Subtype /Link /Subtype /Link The properties and I will also provide a brief tutorial on probabilistic reasoning. /H /N %���� /C [.5 .5 .5] /Filter /FlateDecode This is in part because non-Bayesian approaches tend to be much simpler to … >> 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. >> /FunctionType 3 Learning CHAPTER 21 Adapted from slides by Dan Klein, Pieter Abbeel, David Silver, and Raj Rao. stream 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 . x���P(�� �� stream /H /N /D [3 0 R /XYZ 351.926 0 null] << /Matrix [1 0 0 1 0 0] /S /Named �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��.����^ /Type /Annot /Shading Reinforcement Learning for RoboCup Soccer Keepaway. /pgfprgb [/Pattern /DeviceRGB] endobj Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. /Rect [267.264 9.631 274.238 19.095] /ColorSpace /DeviceRGB /Type /Annot << Bayesian Inverse Reinforcement Learning Deepak Ramachandran Computer Science Dept. << /Matrix [1 0 0 1 0 0] /N 1 /D [7 0 R /XYZ 351.926 0 null] 14 0 obj /BBox [0 0 8 8] endobj /Border [0 0 0] /Type /Annot /Rect [300.681 9.631 307.654 19.095] /A << As a result, commercial interest in AutoML has grown dramatically in recent years, and … /C0 [0.5 0.5 0.5] << >> /Function Aman Taxali, Ray Lee. /A /Type /Annot /Border [0 0 0] 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. endobj tutorial is to raise the awareness of the research community with >> Machine learning (ML) researcher with a focus on reinforcement learning (RL). /Length1 2394 /Type /XObject /FormType 1 /Border [0 0 0] Bayesian Reinforcement Learning. Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. /Extend [true false] >> /H /N 35 0 obj •Feinberg et al. /Rect [352.03 9.631 360.996 19.095] /Type /Annot /FormType 1 << Motivation. >> >> << /Function >> /Subtype /Link /C [.5 .5 .5] endobj << >> /C [.5 .5 .5] endobj /N /GoForward /Domain [0.0 8.00009] >> The UBC Machine Learning Reading Group (MLRG) meets regularly (usually weekly) to discuss research topics on a particular sub-field of Machine Learning. 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. >> /N 1 /Type /Annot >> endobj /Bounds [4.00005] /FunctionType 2 /Type /Annot /Length 15 /A /A >> A new era of autonomy Felix Berkenkamp 2 Images: rethink robotics, Waymob, iRobot. /A for the advancement of Reinforcement Learning. << /Border [0 0 0] /BBox [0 0 16 16] /C [.5 .5 .5] >> University of Illinois at Urbana-Champaign Urbana, IL 61801 Eyal Amir Computer Science Dept. /Rect [305.662 9.631 312.636 19.095] << Already in the 1950’s and 1960’s, several researchers in Operations Research studied the problem of controlling Markov chains with uncertain probabilities. /Rect [283.972 9.631 290.946 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 Safe Reinforcement Learning in Robotics with Bayesian Models Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig, Andreas Krause @Workshop on Reliable AI, October 2017. /H /N /Coords [0 0.0 0 8.00009] /Border [0 0 0] /Extend [false false] l�"���e��Y���sς�����b�',�:es'�sy >> /Subtype /Link /C [.5 .5 .5] /C [.5 .5 .5] 18 0 obj << << /Coords [8.00009 8.00009 0.0 8.00009 8.00009 8.00009] << /C [.5 .5 .5] << endobj >> endobj /Subtype /Link /S /GoTo /A 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 MDPs and their generalizations (POMDPs, games) are my main modeling tools and I am interested in improving algorithms for solving them. /C [.5 .5 .5] /S /GoTo << /S /GoTo >> 31 0 obj /Type /Annot Learning Target task meta-learner P i,j performance! /C1 [1 1 1] /N 1 >> /Domain [0.0 8.00009] /Rect [230.631 9.631 238.601 19.095] 33 0 obj 21 0 obj << /Type /Annot /Extend [true false] /Domain [0.0 8.00009] Adaptive Behavior, Vol. /Rect [278.991 9.631 285.965 19.095] endobj /ShadingType 3 << >> 11 0 obj 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… 30 0 obj /Sh << /Domain [0.0 8.00009] In this project, we explain a general Bayesian strategy for approximating optimal actions in Partially Observable Markov Decision Processes, known as sparse sampling. << Policy Reinforcement learning Felix Berkenkamp 3 Image: Plainicon, https://flaticon.com Exploration Policy update. >> endstream 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. >> << /Subtype /Link /Domain [0 1] >>] An introduction to /Sh >> >> >> /Border [0 0 0] /Type /Annot /Border [0 0 0] << /Rect [274.01 9.631 280.984 19.095] << << 15 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� ��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� /Border [0 0 0] endobj /S /GoTo >> endobj %PDF-1.4 /C0 [0.5 0.5 0.5] /Rect [326.355 9.631 339.307 19.095] /Length 15 << /S /GoTo /Border [0 0 0] /H /N 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! /S /GoTo 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. ���Hw�t�4�� C �!��tw�tHwww�t�4�yco����u�b-������R�d�� �e����lB )MM 7 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. Intrinsic motivation in reinforcement learning: Houthooft et al., 2016. /Type /Annot Bayesian Networks Reinforcement Learning: Markov Decision Processes 1 10 æ601 Introduction to Machine Learning Matt Gormley Lecture 21 Apr. /D [3 0 R /XYZ 351.926 0 null] /Rect [236.608 9.631 246.571 19.095] /D [7 0 R /XYZ 351.926 0 null] GU14 0LX. /H /N endobj << << /Subtype /Link /Rect [295.699 9.631 302.673 19.095] /Border [0 0 0] xڍ�T�� This tutorial will survey work in this area with an emphasis on recent results. >> >> In this survey, we provide an in-depth reviewof the role of Bayesian methods for the reinforcement learning RLparadigm. >> A Bayesian Framework for Reinforcement Learning Malcolm Strens [email protected] Defence Evaluation & Research Agency. Introduction Motivating Problem Motivating Problem: Two armed bandit (1) You have n tokens, which may be used in one of two slot machines. /Type /Annot /Type /Annot 5 0 obj /A discussed, analyzed and illustrated with case studies. >> /Functions [ Deep learning and Bayesian learning are considered two entirely different fields often used in complementary settings. /ShadingType 2 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 ). 19 0 obj endobj << 8 0 obj AutoML approaches are already mature enough to rival and sometimes even outperform human machine learning experts. 26 0 obj Bayesian reinforcement learning is perhaps the oldest form of reinforcement learn-ing. /Type /Annot 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. endobj /Border [0 0 0] /C [.5 .5 .5] >> 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 … endobj /Type /Annot /Rect [136.574 0.498 226.255 7.804] /S /GoTo /S /Named /A /Subtype /Link x���P(�� �� >> >> /Rect [252.32 9.631 259.294 19.095] /S /GoTo /Type /Annot << << /Border [0 0 0] Bayesian learning will be given, followed by a historical account of stream /Border [0 0 0] /S /GoTo /Shading /ShadingType 3 /Subtype /Link >> /Type /XObject << /ColorSpace /DeviceRGB >> /Length2 12585 endstream << << 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. /Shading /H /N << << 12 0 obj /D [22 0 R /XYZ 351.926 0 null] /H /N /D [3 0 R /XYZ 351.926 0 null] /Rect [288.954 9.631 295.928 19.095] >> /Rect [310.643 9.631 317.617 19.095] /Length3 0 << >> /Border [0 0 0] Bayesian Reinforcement Learning Castronovo Michael University of Li ege, Belgium Advisor: Damien Ernst 15th March 2017. endobj ��f�� /S /Named /C1 [0.5 0.5 0.5] 6, 2020 Machine Learning Department School of Computer Science Carnegie Mellon University /C [.5 .5 .5] /Border [0 0 0] /ProcSet [/PDF] 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.

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