bayesian reinforcement learning slides

<< /A Dangers of … /N 1 /A /Type /Annot /Type /Annot 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. /C0 [0.5 0.5 0.5] /Encode [0 1 0 1] >> /Subtype /Form /C1 [0.5 0.5 0.5] >> �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 /XObject What Independencies does a Bayes Net Model? 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. >> AutoML approaches are already mature enough to rival and sometimes even outperform human machine learning experts. 11 0 obj /Subtype /Link Policy Reinforcement learning Felix Berkenkamp 3 Image: Plainicon, https://flaticon.com Exploration Policy update. ��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 /A << ��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� /N /GoToPage << 23 0 obj ���Hw�t�4�� C �!��tw�tHwww�t�4�yco����u�b-������R�d�� �e����lB )MM 7 /FormType 1 Modern Deep Learning through Bayesian Eyes Yarin Gal yg279@cam.ac.uk To keep things interesting, a photo or an equation in every slide! Intrinsic motivation in reinforcement learning: Houthooft et al., 2016. /Type /Annot >> >> endobj /Filter /FlateDecode /Subtype /Link /Rect [339.078 9.631 348.045 19.095] /Subtype /Link /Resources 31 0 R 24 0 obj In model-based reinforcement learning, an agent uses its experience to construct a representation of the control dynamics of its environment. /Subtype /Link /Resources 35 0 R /H /N 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 ). Introduction Motivating Problem Motivating Problem: Two armed bandit (1) You have n tokens, which may be used in one of two slot machines. >> The UBC Machine Learning Reading Group (MLRG) meets regularly (usually weekly) to discuss research topics on a particular sub-field of Machine Learning. 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. /ProcSet [/PDF] << >> /Rect [278.991 9.631 285.965 19.095] << >> /Domain [0.0 8.00009] /S /Named /Rect [230.631 9.631 238.601 19.095] /Rect [262.283 9.631 269.257 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. >> endobj discussed, analyzed and illustrated with case studies. /Subtype /Link graphics, and that Bayesian machine learning can provide powerful tools. /A endobj /Shading /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. /N /GoBack /C1 [0.5 0.5 0.5] << for the advancement of Reinforcement Learning. Model-Based Bayesian RL slides adapted from: Poupart ICML 2007. /BBox [0 0 8 8] x���P(�� �� endobj /C [.5 .5 .5] /S /Named %���� >> >> stream /N /GoForward << /C [.5 .5 .5] /H /N >> << Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. /H /N /S /Named /BBox [0 0 5669.291 8] I … /D [3 0 R /XYZ 351.926 0 null] •Chua et al. endobj >> To join the mailing list, please use an academic email address and send an email to majordomo@cs.ubc.ca with an […] >> /Rect [252.32 9.631 259.294 19.095] The properties and r�����l�h��r�X�� 5Ye6WOW����_��v.`����)���b�w� Y�7 S�鹘;�]]�\@vQd�+��2R`{{����_�I���搶{��3Y[���Ͽ��`a� 7Gvm��PA�_��� /S /GoTo endobj 4 0 obj Safe Reinforcement Learning in Robotics with Bayesian Models Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig, Andreas Krause @Workshop on Reliable AI, October 2017. /A /D [3 0 R /XYZ 351.926 0 null] /C1 [1 1 1] stream endobj /C [.5 .5 .5] 16 0 obj << /Border [0 0 0] /Domain [0.0 8.00009] /Functions [ /D [3 0 R /XYZ 351.926 0 null] /Border [0 0 0] << << 8 0 obj /pgfprgb [/Pattern /DeviceRGB] << /Subtype /Link /S /GoTo /D [3 0 R /XYZ 351.926 0 null] Bayesian Inverse Reinforcement Learning Deepak Ramachandran Computer Science Dept. /Border [0 0 0] 13 0 obj /H /N /ColorSpace /DeviceRGB /C [.5 .5 .5] << /D [22 0 R /XYZ 351.926 0 null] /A /Extend [true false] /Rect [295.699 9.631 302.673 19.095] Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. 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 10 0 obj 18 0 obj Lecture slides will be made available here, together with suggested readings. endobj /N /Find /C0 [0.5 0.5 0.5] >> This is in part because non-Bayesian approaches tend to be much simpler to … /ColorSpace /DeviceRGB 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 … /S /GoTo >> /Length 15 /Rect [305.662 9.631 312.636 19.095] /Border [0 0 0] >> /Filter /FlateDecode /H /N x���P(�� �� /Type /Annot /S /GoTo Bayesian Reinforcement Learning and a description of existing Variational information maximizing exploration Network compression: Louizos et al., 2017. << /D [3 0 R /XYZ 351.926 0 null] 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. << /Subtype /Link endobj << 21 0 obj /Rect [310.643 9.631 317.617 19.095] >> /A endobj >> /S /GoTo /Domain [0.0 8.00009] endobj /A /Sh 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 . 1052A, A2 Building, DERA, Farnborough, Hampshire. /Type /Annot tutorial is to raise the awareness of the research community with endobj << /A 28 0 obj /Border [0 0 0] << /Functions [ /FunctionType 2 /Type /Annot (unless specified otherwise, photos are either original work or taken from Wikimedia, under Creative Commons license) /Rect [288.954 9.631 295.928 19.095] 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. /Border [0 0 0] >> << /N 1 /C [.5 .5 .5] << /C [.5 .5 .5] << /Type /Annot /Length 15 /ShadingType 2 /Type /XObject /S /GoTo /Subtype /Link /S /GoTo 6 0 obj /C [.5 .5 .5] /Subtype /Link /C [.5 .5 .5] •Buckman et al. Our experimental results confirm the greedy-optimal behavior of this methodology. /Border [0 0 0] /Border [0 0 0] /Type /Annot << GU14 0LX. << /A 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 . •Feinberg et al. /Filter /FlateDecode The primary goal of this /Sh 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 xڍ�T�� /C [1 0 0] >> >> Motivation. /H /N 30 0 obj /D [3 0 R /XYZ 351.926 0 null] << /Type /Annot << /Subtype /Link 25 0 obj 35 0 obj << Bayesian Networks Reinforcement Learning: Markov Decision Processes 1 10 æ601 Introduction to Machine Learning Matt Gormley Lecture 21 Apr. 19 0 obj /ProcSet [/PDF] << >> 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 … Aman Taxali, Ray Lee. /C [.5 .5 .5] 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… << As a result, commercial interest in AutoML has grown dramatically in recent years, and … << Machine learning (ML) researcher with a focus on reinforcement learning (RL). /A >> /C [.5 .5 .5] /A Introduction to Reinforcement Learning and Bayesian learning. >>] /H /N /H /N /Extend [true false] >> /C [1 0 0] l�"���e��Y���sς�����b�',�:es'�sy >> << It can then predict the outcome of its actions and make decisions that maximize its learning and task performance. /FormType 1 /Border [0 0 0] /D [3 0 R /XYZ 351.926 0 null] /Domain [0.0 8.00009] /H /N /Domain [0.0 8.00009] /Type /Annot Probabilistic & Bayesian deep learning Andreas Damianou Amazon Research Cambridge, UK Talk at University of She eld, 19 March 2019. 15 0 obj /A ��0��;��H��m��ᵵ�����yJ=�|�!��xފT�#���q�� .Pt���Rűa%�pe��4�2ifEڍ�^�'����BQtQ��%���gt�\����b >�v�Q�$2�S�rV(/�3�*5�Q7�����~�I��}8�pz�@!.��XI��#���J�o��b�6k:�����6å4�+��-c�(�s�c��x�|��"��)�~8H�(ҁG�Q�N��������y��y�5飌��ڋ�YLZ��^��D[�9�B5��A�Eq� Bayesian methods for Reinforcement Learning. In this project, we explain a general Bayesian strategy for approximating optimal actions in Partially Observable Markov Decision Processes, known as sparse sampling. endobj << /ColorSpace /DeviceRGB I will also provide a brief tutorial on probabilistic reasoning. /Shading endobj << /ProcSet [/PDF] /C [.5 .5 .5] /Domain [0.0 8.00009] << /C1 [1 1 1] >> 33 0 obj /S /Named /FunctionType 2 >> endobj 6, 2020 Machine Learning Department School of Computer Science Carnegie Mellon University >> /C [1 0 0] /Rect [236.608 9.631 246.571 19.095] /D [3 0 R /XYZ 351.926 0 null] << �v��`�Dk����]�dߍ��w�_�[j^��'��/��Il�ت��lLvj2.~����?��W�T��B@��j�b������+��׭�a��yʃGR���6���U������]��=�0 QXZ ��Q��@�7��좙#W+�L��D��m�W>�m�8�%G䱹,��}v�T��:�8��>���wxk �վ�L��R{|{Յ����]�q�#m�A��� �Y魶���a���P�<5��/���"yx�3�E!��?o%�c��~ݕI�LIhkNҜ��,{�v8]�&���-��˻L����{����l(�Q��Ob���*al3܆Cr�ͼnN7p�$��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��.����^ 32 0 obj /FormType 1 >> /C [.5 .5 .5] /N 1 endstream /Subtype /Link /Subtype /Link endobj >> MDPs and their generalizations (POMDPs, games) are my main modeling tools and I am interested in improving algorithms for solving them. >> /Function /Type /Annot >> >> << /Length3 0 >>] A Bayesian Framework for Reinforcement Learning Malcolm Strens MJSTRENS@DERA.GOV.UK Defence Evaluation & Research Agency. Bayesian reinforcement learning is perhaps the oldest form of reinforcement learn-ing. endstream In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. /Rect [257.302 9.631 264.275 19.095] << /FunctionType 2 /D [7 0 R /XYZ 351.926 0 null] /Border [0 0 0] /FunctionType 3 /Rect [346.052 9.631 354.022 19.095] >> 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! >> >> /Border [0 0 0] /Border [0 0 0] In this talk, I will discuss the main challenges of robot learning, and how BO helps to overcome some of them. /Rect [244.578 9.631 252.549 19.095] /Matrix [1 0 0 1 0 0] >> endobj University of Illinois at Urbana-Champaign Urbana, IL 61801 Eyal Amir Computer Science Dept. /C [.5 .5 .5] /Border [0 0 0] 9 0 obj 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. << endobj /Type /Annot Learning Target task meta-learner P i,j performance! /Border [0 0 0] 29 0 obj endobj /C [.5 .5 .5] /Domain [0.0 8.00009] << /Coords [0 0.0 0 8.00009] /Length 15 /C [.5 .5 .5] >> /Function /A Bayesian optimization has shown to be a successful approach to automate these tasks with little human expertise required. 39 0 obj benefits of Bayesian techniques for Reinforcement Learning will be GRAPHICAL MODELS: DETERMINING CONDITIONAL INDEPENDENCIES. /Border [0 0 0] stream /Rect [300.681 9.631 307.654 19.095] /Border [0 0 0] 5 0 obj regard to Bayesian methods, their properties and potential benefits /Bounds [4.00005] /A A new era of autonomy Felix Berkenkamp 2 Images: rethink robotics, Waymob, iRobot. /Border [0 0 0] 13, No. /Sh /Border [0 0 0] /C0 [0.5 0.5 0.5] /D [7 0 R /XYZ 351.926 0 null] /Subtype /Form /Domain [0.0 8.00009] /H /N /ShadingType 3 << endobj endobj 34 0 obj >> /S /GoTo 31 0 obj /Subtype /Link /H /N 3, 2005 RL = learning meets planning endobj /Subtype /Link /H /N /A << /C [.5 .5 .5] Bayesian learning will be given, followed by a historical account of << /S /GoTo /Subtype /Link /Rect [267.264 9.631 274.238 19.095] /Length2 12585 /N 1 << ������ � @Osk���ky9�V�-�0��q;,!$�~ K �����;������S���`2w��@(��C�@�0d�� O�d�8}���w��� ;�y�6�{��zjZ2���0��NR� �a���r�r 89�� �|� �� ������RuSп�q����` ��Ҽ��p�w-�=F��fPCv`������o����o��{�W������ɺ����f�[���6��y�k Ye7W�Y��!���Mu���� /Type /Annot >> /Type /Annot >> /Type /Annot /H /N << /Subtype /Link N�>40�G�D�+do��Y�F�����$���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 << /S /GoTo /Matrix [1 0 0 1 0 0] 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. >> =?�%�寉B��]�/�?��.��إ~# ��o$`��/�� ���F� v�߈���A�)�F�|ʿ$��oɠ�_$ ɠ�A2���� ��$��o�`��� �t��!�L#?�����������t�-��������R��oIkr6w�����?b^Hs�d�����ey�~����[�!� G�0 �Ob���Nn����i��o1�� y!,A��������?������wŐ Z{9Z����@@Hcm���V���A���qu�l�zH����!���QC�w���s�|�9���x8�����x �t�����0������h/���{�>.v�.�����]�Idw�v�1W��n@H;�����x��\�x^@H{�Wq�:���s7gH\�~�!���ߟ�@�'�eil.lS�z_%A���;�����)V�/�וn᳏�2b�ܴ���E9�H��bq�Լ/)�����aWf�z�|�+�L߶�k���U���Lb5���i��}����G�n����/��.�o�����XTɤ�Q���0�T4�����X�8��nZ endstream /C1 [0.5 0.5 0.5] ����p���oA.� O��:������� ��@@u��������t��3��B��S�8��-�:����� << >> Bayesian Reinforcement Learning Nikos Vlassis, Mohammad Ghavamzadeh, Shie Mannor, and Pascal Poupart AbstractThis chapter surveys recent lines of work that use Bayesian techniques for reinforcement learning. /Subtype /Link N�>40�G�D�+do��Y�F�����$���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 /Length 13967 /Bounds [4.00005] << << << /Coords [4.00005 4.00005 0.0 4.00005 4.00005 4.00005] endobj >> >> d����\�������9�]!. /A /H /N Bayesian RL: Why - Exploration-Exploitation Trade-off - Posterior: current representation of … /Subtype /Link Bayesian Reinforcement Learning. /Matrix [1 0 0 1 0 0] /FunctionType 2 /Type /Annot /Subtype /Link >> << /Border [0 0 0] /Encode [0 1 0 1] /H /N >> /Subtype /Form << /Domain [0 1] /Rect [283.972 9.631 290.946 19.095] >> /C0 [0.5 0.5 0.5] 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. /Type /Annot 17 0 obj /Shading Adaptive Behavior, Vol. << /Coords [8.00009 8.00009 0.0 8.00009 8.00009 8.00009] /D [22 0 R /XYZ 351.926 0 null] Introduction What is Reinforcement Learning (RL)? 26 0 obj /Length1 2394 << /D [3 0 R /XYZ 351.926 0 null] • In order for a Bayesian network to model a probability distribution, the … Contents Introduction Problem Statement O ine Prior-based Policy-search (OPPS) Arti cial Neural Networks for BRL (ANN-BRL) Benchmarking for BRL Conclusion 2. � /FunctionType 2 Bayesian methods for machine learning have been widely investigated,yielding principled methods for incorporating prior information intoinference algorithms. /Resources 33 0 R Deep learning and Bayesian learning are considered two entirely different fields often used in complementary settings. /H /N >> << 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 [352.03 9.631 360.996 19.095] Model-Based Value Expansion for Efficient Model-Free Reinforcement Learning. /Type /Annot >> << endobj >> l�"���e��Y���sς�����b�',�:es'�sy >> This tutorial will survey work in this area with an emphasis on recent results. /C [1 0 0] /D [3 0 R /XYZ 351.926 0 null] /Subtype /Link Subscription You can receive announcements about the reading group by joining our mailing list. /Extend [false false] /Rect [317.389 9.631 328.348 19.095] 12 0 obj >> endobj /Border [0 0 0] endobj /H /N ��K;&������oZi�i��f�F;�����*>�L�N��;�6β���w��/.�Ҥ���2�G��T�p�…�kJc؎�������!�TF;m��Y��CĴ�. endobj /D [3 0 R /XYZ 351.926 0 null] In this survey, we provide an in-depth reviewof the role of Bayesian methods for the reinforcement learning RLparadigm. << /A Reinforcement Learning with Model-Free Fine-Tuning. This tutorial will introduce modern Bayesian principles to bridge this gap. >> /S /GoTo << endobj 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. An introduction to /S /GoTo /D [7 0 R /XYZ 351.926 0 null] /Filter /FlateDecode /S /GoTo /Type /Annot << It is clear that combining ideas from the two fields would be beneficial, but how can we achieve this given their fundamental differences? /H /N /Subtype /Link /ShadingType 3 /Rect [326.355 9.631 339.307 19.095] << /Type /XObject /Type /Annot >> 20 0 obj >> /Rect [136.574 0.498 226.255 7.804] Already in the 1950’s and 1960’s, several researchers in Operations Research studied the problem of controlling Markov chains with uncertain probabilities. << /C [.5 .5 .5] Bayesian Reinforcement Learning Castronovo Michael University of Li ege, Belgium Advisor: Damien Ernst 15th March 2017. << << >> %PDF-1.4 /Function >> /H /N /S /GoTo x���P(�� �� 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. /S /GoTo /S /GoTo /N 1 /FunctionType 3 -������V��;�a �4u�ȤM]!v*`�������'��/�������!�Y m�� ���@Z)���3�����?������,�$�� sS����5������ 6]��'������;��������J���r�h ]���@�_�����������A.��5�����@ D`2:�@,�� Hr���2@������?,�{�d��o��� endobj << /D [7 0 R /XYZ 351.926 0 null] /H /N Learning CHAPTER 21 Adapted from slides by Dan Klein, Pieter Abbeel, David Silver, and Raj Rao. 14 0 obj /Type /Annot /Domain [0 1] Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. >> /A Reinforcement Learning for RoboCup Soccer Keepaway. >> /Rect [274.01 9.631 280.984 19.095] /S /GoTo << >> << /Border [0 0 0] /H /N stream /BBox [0 0 16 16] ��f�� /C0 [1 1 1]

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