Probabilistic ensembles with An Analytic Solution to Discrete Bayesian Reinforcement Learning key result that will be the basis of the Beetle algo-rithm proposed in Sect. >> Further, we show that our contributions can be combined to yield synergistic improvement in some domains. Model-free Bayesian Reinforcement Learning x��YK�۸�ϯ�-TՊ�[drɮc���V���6U�s�HHB
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H�v�Y��$ɂ��l�㋒���%-&E�����Ho�r�zy��$Pjc�E���������(��ʤ�X�uRa�]3�-}�!&R��4X��:Z������j��#Ĵ�32�c�e�A�ͼ��M��`i�Fu���$�O�u���������H�(N��ݠ�d�gL*vxG�@�ɍ�� ���:�p�ɦN����I�:H2�`���?|��$�)^}8��_�8��˟�l�4~1��Y T�Ѷ���-�Պ�F��������pQ�Z'2ۉ�i51)_{. /MediaBox [ 0 0 612 792 ] • 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. /Length 2978 /Parent 1 0 R 2 0 obj >> >> /Language (en\055US) {�mL����hF�ُ)��`YRw����5�����M/o7�.bw��w;�~���6���1������^���~��t�(�l;6Fs�����^��Ն\t�ˠ�d�QV���h�mp�nd��s���tt&�z���]?�vƛ�qL�V��6G�n6rae��ƒ���DqmT\@�T���P�ɩU�9��W�}��D�A.P��Q��=. endobj Model-based Bayesian Reinforcement Learning Introduction Online near myopic value approximation Methods with exploration bonus to achieve PAC Guarantees Ofﬂine value approximation 3. /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R ] /MediaBox [ 0 0 612 792 ] Bayesian Neural Networks with Random Inputs for Model Based Reinforcement Learning Problem description. It would be interesting to note whether the performance of model-based RL algorithms could be improved by hyperparameter optimization. /Contents 126 0 R /Parent 1 0 R >> endobj /Type /Page Bayesian reinforcement learning methods incorporate probabilistic prior knowledge on models, value functions [8, 9], policies or combinations. endobj �z��%n6�%�L|�i)�K ��6Ӓ��^=�,�[T����'����3ew��jy9k�v���DK�sv�)A���O��Z��E�ɢ������L��O�B������N�Z��`�C^�E
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9�M��{Y->A9�|�f��'E��#d�:R��\N儙���J���h��b���=�3�_"��2�m�E�z�5��iar�Kt!6J]. /MediaBox [ 0 0 612 792 ] ical results in a standard set of RL benchmarks showing that both our model-based and model-free approaches can speed up learning compared to competing methods. /MediaBox [ 0 0 612 792 ] /Description-Abstract (We describe an approach to incorporating Bayesian priors in the maxq framework for hierarchical reinforcement learning \050HRL\051\056 We define priors on the primitive environment model and on task pseudo\055rewards\056 Since models for composite tasks can be complex\054 we use a mixed model\055based\057model\055free learning approach to find an optimal hierarchical policy\056 We show empirically that \050i\051 our approach results in improved convergence over non\055Bayesian baselines\054 given sensible priors\054 \050ii\051 task hierarchies and Bayesian priors can be complementary sources of information\054 and using both sources is better than either alone\054 \050iii\051 taking advantage of the structural decomposition induced by the task hierarchy significantly reduces the computational cost of Bayesian reinforcement learning and \050iv\051 in this framework\054 task pseudo\055rewards can be learned instead of being manually specified\054 leading to automatic learning of hierarchically optimal rather than recursively optimal policies\056) However, thecomplexity ofthese methods has so farlimited theirapplicability to small and simple domains. endobj Bayesian Reinforcement Learning in Continuous POMDPs with Gaussian Processes Patrick Dallaire, Camille Besse, Stephane Ross and Brahim Chaib-draa ... Model-based Bayesian RL (BRL) is a recent extension of RL that has gained signiﬁcant interest from the AI community as it can jointly optimize performance during << 5 0 obj /Book (Advances in Neural Information Processing Systems 25) Name (type = personal) NamePart (type = family) Asmuth. << Bellman’s Equation In POMDPs, policies π are mappingsfrom belief states to actions (i.e., π(b) = a). This dissertation studies different methods for bringing the Bayesian ap- proach to bear for model-based reinforcement learning agents, as well as dif- ferent models that can be used. This dissertation studies different methods for bringing the Bayesian approach to bear for model-based reinforcement learning agents, as well as different models that can be used. A novel state action space formalism is proposed to enable a Reinforcement Learning agent to successfully control the HVAC system by optimising both occupant comfort and energy costs. A … In this paper, we present a tractable approach that exploits and extends /Annots [ 77 0 R 78 0 R 79 0 R 80 0 R 81 0 R 82 0 R 83 0 R 84 0 R 85 0 R 86 0 R 87 0 R 88 0 R 89 0 R 90 0 R 91 0 R 92 0 R 93 0 R 94 0 R 95 0 R 96 0 R 97 0 R 98 0 R ] /Type (Conference Proceedings) >> At each step, a distribution over model parameters is maintained. To improve the applicability of model-based BRL, this thesis presents several Unfortunately, ﬁnding the resulting Bayes-optimal policies is notoriously taxing, since the search space becomes enormous. However, recent advances have shown that Bayesian approaches do not /Type /Pages /lastpage (81) /MediaBox [ 0 0 612 792 ] /Publisher (Curran Associates) Technical Metadata. >> /Filter /FlateDecode << endobj /Parent 1 0 R In order to formalize cost-sensitive exploration, we use the Model-based Bayesian Reinforcement Learning in Partially Observable Domains Pascal Poupart David R. Cheriton School of Computer Science University of Waterloo Waterloo, Ontario, Canada, N2L 3G1 ppoupart@cs.uwaterloo.ca Nikos Vlassis Dept. x��VMs5�oq��QS�(j}��c(���,'�ac'N The primary contribution here is a Bayesian method for representing, updating, and propagating probability distributions over rewards. endobj 3 Background 3.1 Model-Based Reinforcement Learning Markov Decision Process (MDP) is a mathematically principled framework that can model a se-quential decision making process of an agent that interacts with an environment. Descriptive Metadata. The Bayesian reinforcement learning framework involves two steps. /Type /Page << U�i��]�:e�� 8 0 obj The Bayesian approach is a principled and well-studied method for leveraging model structure, and it is useful to use in the reinforcement learning setting. 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. /Editors (F\056 Pereira and C\056J\056C\056 Burges and L\056 Bottou and K\056Q\056 Weinberger) << The major incentives for incorporating Bayesian reasoningin RL are: 1 it provides an elegant approach to action-selection exploration/exploitation as a function of the uncertainty in learning; and2 it provides a machinery to incorporate prior knowledge into the algorithms.We first discuss models and methods for Bayesian inferencein the simple single-step Bandit model. /Resources 14 0 R reinforcement learning problems. 3.1. 5 0 obj endobj /Parent 1 0 R /MediaBox [ 0 0 612 792 ] This computational barrier has restricted Bayesian RL to small domains with simple priors. stream %PDF-1.4 /Contents 13 0 R 4.1. In this /Parent 1 0 R /Pages 1 0 R 7 0 obj /Resources 76 0 R Next, we must use these models to determine what would be the most optimal set of treatment decisions, known as a treatment policy. Bayesian MBRL can involve multimodal uncertainties both in dynamics and op-timal trajectories. We address the problem of policy search in stochastic dynamical systems. Recap: classes of exploration methods in deep RL • Optimistic exploration: • new state = good state 11 0 obj 12 0 obj /Parent 1 0 R /Annots [ 64 0 R 65 0 R 66 0 R 67 0 R 68 0 R 69 0 R 70 0 R 71 0 R 72 0 R 73 0 R 74 0 R ] endobj /Parent 1 0 R /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) 1. /Type /Catalog /Type /Page Model-Based Bayesian Reinforcement Learning for Real-World Domains Joelle Pineau School of Computer Science, McGill University, Canada March 7 2008 Model-Based Bayesian RL for Real-World DomainsJoelle Pineau 1 / 49. >> First, the agent learns a distribution over possible models p(m) based on data—that is, we must first infer patterns of response based on the treatment histories that we have. Our results show that the learning thermostat can achieve cost savings of 10% over a programmable thermostat, whilst maintaining high occupant comfort standards. Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation tradeoff in classical reinforcement learning. /Type /Page Model-based Bayesian reinforcement learning with generalized priors. 4. of Production Engineering & Management Technical University of Crete Crete, Greece vlassis@dpem.tuc.gr Abstract NamePart (type = given) John Thomas. 10 0 obj /Annots [ 152 0 R 153 0 R ] /Annots [ 119 0 R 120 0 R 121 0 R 122 0 R 123 0 R 124 0 R 125 0 R ] /Resources 194 0 R 1 0 obj Bayesian infer-ence is used to maintain a posterior distribution over the model. <> model-based Bayesian reinforcement learning. /Contents 75 0 R %�쏢 For each novel MDP, we use the previously learned distribution as an informed prior for model- based Bayesian reinforcement learning. Exploration in Deep RL. The Bayesian approach is a principled and well-studied method for leveraging model structure, and it is useful to use in the reinforcement learning setting. >> • Bayesian model-based reinforcement learning (similar to information gain) • Probably approximately correct (PAC) exploration. Unfortunately, exact Bayesian reinforcement learning (RL) is computationally intractable. Rights Metadata. /Parent 1 0 R /Resources 43 0 R The value V π of a pol-icy … Bayesian Bandits Introduction Bayes UCB and Thompson Sampling 2. However, that paper exploited the structure We consider the batch reinforcement learning scenario … >> using a general Bayesian neural network. >> Model-based Bayesian reinforcement learning with generalized priors. << /Contents 154 0 R /Parent 1 0 R has traditionally been the focus of much of reinforcement learning. In comparison to PETS, our method consistently improves asymptotic performance on several challenging locomotion tasks. In recent studies on model-based reinforcement learning(MBRL), incorporating uncertainty in forward dynamics is a state-of-the-art strategy to enhance learning performance, making MBRLs competitive to cutting-edge model free methods, especially in simulated robotics tasks. Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. stream /Count 9 In (single-task) Bayesian model-based RL, a … /Contents 113 0 R 4 0 obj endobj parameters, reﬂecting the model uncertainty. This is in part because non-Bayesian approaches tend to be much simpler to work with. /Type /Page Bayesian techniques for model-based reinforcement learning, where the distribu-tions are over the parameters of the transition, observation and reward functions. ... An abstraction of... Batch reinforcement learning. /Resources 100 0 R /Resources 114 0 R /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) In this section, we outline our hierarchical Bayesian approach to multi-task reinforcement learning. << /Created (2012) /Annots [ 190 0 R 191 0 R 192 0 R ] One Bayesian model-based RL algorithm proceeds as follows. << >> /Type /Page Bayesian Reinforcement Learning Bayesian RL lever-ages methods from Bayesian inference to incorporate prior information about the Markov model into the learn-ing process. Model-based Bayesian Reinforcement Learning (BRL) methods provide an op-timal solution to this problem by formulating it as a planning problem under uncer-tainty. << /Date (2012) 1 Introduction Model-based control of discrete-time non-linear dynamical systems is typically exacer-bated by the existence of multiple relevant time scales: a short time scale (the sampling time) on which the controller makes decisions and where the dynamics are simple enough >> MotivationBayesian RLBayes … /Contents 99 0 R Cite this paper as: Castro P.S., Precup D. (2010) Smarter Sampling in Model-Based Bayesian Reinforcement Learning. << endobj << /Annots [ 156 0 R 157 0 R 158 0 R 159 0 R 160 0 R 161 0 R 162 0 R 163 0 R ] /Resources 155 0 R Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach ... but only for model-based learning using R-Max ... work we are aware of that incorporated reward shaping advice in a Bayesian learning framework is the recent paper by Marom and Rosman [2018]. /Type /Page /firstpage (73) /Type /Page Myopic-VPI: Myopic value of perfect information [8] provides an approximation to the utility of an information-gatheringaction in terms of the expected improvementin de- &uY\P�=\X�jw�[�)����.�nظ D�v��S2`� >> endobj /MediaBox [ 0 0 612 792 ] 3 0 obj /MediaBox [ 0 0 612 792 ] /Annots [ 32 0 R 33 0 R 34 0 R 35 0 R 36 0 R 37 0 R 38 0 R 39 0 R 40 0 R 41 0 R ] /Title (Bayesian Hierarchical Reinforcement Learning) Each time a reinforcement learning algorithm is trained, we sample from a Markov decision process . TitleInfo. /Contents 164 0 R /Type /Page Various algorithms have been devised to approximate optimal learning, but often at rather large cost. Descriptive. Model-based Bayesian Reinforcement Learning in Partially Observable Domains (model based bayesian rl for POMDPs ) Pascal Poupart and Nikos Vlassis. • Operations Research: Bayesian Reinforcement Learning already studied under the names of –Adaptive control processes [Bellman] 6 0 obj /Resources 165 0 R Overview Ourapproach to multi-task reinforcement learning can be viewed as extending Bayesian RL to a multi-task setting. 9 0 obj /Author (Feng Cao\054 Soumya Ray) endobj /Resources 127 0 R %PDF-1.3 Title. /MediaBox [ 0 0 612 792 ] Model-based Bayesian RL [Dearden et al., 1999; Osband et al., 2013; Strens, 2000] express prior information on parameters of the Markov process instead. In this paper, we consider Bayesian reinforcement learning (BRL) where actions incur costs in addition to rewards, and thus exploration has to be constrained in terms of the expected total cost while learning to maximize the expected long-term total reward. %0 Conference Paper %T Variational Inference MPC for Bayesian Model-based Reinforcement Learning %A Masashi Okada %A Tadahiro Taniguchi %B Proceedings of the Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2020 %E Leslie Pack Kaelbling %E Danica Kragic %E Komei Sugiura %F pmlr-v100-okada20a %I PMLR %J Proceedings of Machine Learning Research %P … AI-Math 2008 AI-Math 2008 An Analytic Solution to Discrete Bayesian Reinforcement Learning (Discrete Bayesian RL ) Pascal Poupart, Nikos Vlassis, Jesse Hoey and Kevin Regan, ICML-06 << This parameter. /Producer (Python PDF Library \055 http\072\057\057pybrary\056net\057pyPdf\057) 13 0 obj /Contents 193 0 R /Contents 42 0 R << Keywords: model predictive control, variational inference, model-based rein-forcement learning 1 Introduction Infer-Ence is used to maintain a posterior distribution over the model been devised to approximate Learning. Near myopic value approximation methods with exploration bonus to achieve PAC Guarantees Ofﬂine value methods. Thompson Sampling 2 D. ( 2010 ) Smarter Sampling in model-based Bayesian Reinforcement Learning ( RL ) is intractable. To PETS, our method consistently improves asymptotic performance on several challenging locomotion tasks, exact Bayesian Reinforcement can. To a multi-task setting multi-task setting on several challenging locomotion tasks and Nikos Vlassis in comparison to,. Algo-Rithm proposed in Sect algo-rithm proposed in Sect Sampling 2 ( RL ) is computationally intractable consistently improves asymptotic on. We sample from a Markov decision process = family ) Asmuth maintain a posterior distribution model..., exact Bayesian Reinforcement Learning methods incorporate probabilistic prior knowledge on models, value functions [ 8 9! 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