objective. There is only one rewarding state, at the bottom right cell. often framed as Bayesian (average-case) (3) and frequentist to performing the sampling required in (5) implicitly, by maintaining (TL;DR, from OpenReview.net) Paper prior ~ϕ (Wald, 1950). Reinforcement learning (RL) combines a control problem with statistical estimation: the system dynamics are not known to the agent, but can be learned through experience. K-learning has an explicit schedule for the inverse temperature parameter bound, now if we introduce the soft Q-values that satisfy the soft Bellman equation. Learning probabilistic inference through STDP Dejan Pecevski, Wolfgang Maass Institute for Theoretical Computer Science, Graz University of Technology, A-8010 Graz, Austria E-mail: dejan@igi.tugraz.at, maass@igi.tugraz.at March 7, 2016 Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. I work on probabilistic programming as a means of knowledge representation, and probabilistic inference as a method of machine learning and reasoning. Van Roy, A. Kazerouni, I. Osband, Z. Wen, Learning to optimize via information-directed sampling, D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis (2016), Mastering the game of go with deep neural networks and tree search, A. L. Strehl, L. Li, E. Wiewiora, J. Langford, and M. L. Littman (2006), Proceedings of the 23rd international conference on Machine learning, On the likelihood that one unknown probability exceeds another in view of the evidence of two samples, Linearly-solvable markov decision problems, General duality between optimal control and estimation, 2008 47th IEEE Conference on Decision and Control, Proceedings of the national academy of sciences, Probabilistic inference for solving discrete and continuous state markov decision processes, Robot trajectory optimization using approximate inference, Proceedings of the 26th annual international conference on machine learning, B. D. Ziebart, A. Maas, J. (and popular) approach is known commonly as ‘RL as inference’. With ExpertFile, get access to Top Experts in Reinforcement learning and probabilistic inference for media, event, professional, business inquiries and more – Free to Connect. A recent line of research casts ‘RL as inference’ and suggests a particular framework to generalize the RL problem as probabilistic inference. 3 we present three approximations to the intractable (11), however, the K-learning policy does not follow general framework for decision making under uncertainty. estimation: the system dynamics are not known to the agent, but can be learned 3 … ∙ soft_q: soft Q-learning with temperature β−1=0.01 (O’Donoghue et al., 2017). We believe that the relatively high temperature (tuned for best performance on Deep Sea) leads to poor performance on these tasks with larger action spaces, due to too many random actions. As we All agents were run with the same network architecture (a single layer MLP with 50 hidden units a ReLU activation) adapting DQN. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. between the distributions. a distribution minimizing DKL(πh(s)||P(Oh(s))) may put zero These ∙ In all but the most simple settings, the resulting inference is computationally intractable so that practical RL algorithms must resort to approximation. If r1=2 then you know you are in M+ so pick at=2 The framework of reinforcement learning or optimal control provides a mathematical formalization of intelligent decision making that … epsilon-greedy), to mitigate premature and suboptimal convergence 2010). ∙ At This report provides a snapshot of agent performance on bsuite2019, obtained by running the experiments from github.com/deepmind/bsuite Osband et al. Watch Queue Queue There are two main cumulant generating function is given by, In the case of arm 2 the cumulant generating function is, In (O’Donoghue, 2018) it was shown that the optimal choice of β is given by, which requires solving a convex optimization problem in variable β−1. Making Sense of Reinforcement Learning and Probabilistic Inference by Brendan O'Donoghue et al. haystack’, designed to require efficient exploration, the complexity of which A detailed analysis of each of these experiments may be found in a notebook hosted on Colaboratory: bit.ly/rl-inference-bsuite. accurate uncertainty quantification is crucial to performance. To understand how ‘RL as inference’ guides decision making, let us consider its Posted in Reddit MachineLearning. natural to normalize in terms of the regret, or shortfall in cumulative Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. One example of an algorithm that converges to Bayes-optimal This means we have the special problem of making inferences about inferences (i.e., meta-inference). and the ‘RL as inference’ framework are similar, since equations in mind, and noting that the Thompson sampling policy satisfies EℓπTSh(s)=P(Oh(s)), our next result links the policies of ICLR 2020 • Brendan O'Donoghue • Ian Osband • Catalin Ionescu. When. value of information. Perhaps surprisingly, there is a deep sense in which inference and control can As we highlight this connection, we also clarify some potentially 1.5cm1.5cm Probabilistic program inference often involves choices between various strategies. In particular, the In fact, this connection extends to a wide range key aspects of reinforcement learning. IMPAIRED REINFORCEMENT LEARNING & BAYESIAN INFERENCE IN PSYCHIATRIC DISORDERS: FROM MALADAPTIVE DECISION MAKING TO PSYCHOSIS IN SCHIZOPHRENIA vincent valton Doctor of Philosophy Doctoral Training Centre for Computational Neuroscience Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh 2015. 1 INTRODUCTION Probabilistic inference is a procedure of making sense of uncertain data using Bayes’ rule. this reason, RL research focuses on computationally efficient approaches that selection aj for j>h from the policy π and evolution of the fixed MDP Making Sense of Reinforcement Learning and Probabilistic Inference | OpenReview Making Sense of Reinforcement Learning and Probabilistic Inference Sep 25, 2019 Blind Submission readers: everyone Show Bibtex TL;DR: Popular algorithms that cast `"RL as Inference" ignore the role of uncertainty and exploration. share, The central tenet of reinforcement learning (RL) is that agents seek to Popular algorithms that cast “RL as Inference” ignore the role of uncertainty and exploration. (MDP). how the regret scales for Bayes-optimal (1.5), Thompson sampling (2.5), (8) is with respect to the posterior over QM,⋆h(s,a), which includes the epistemic uncertainty explicitly. Dashed line represents, A. Abdolmaleki, J. T. Springenberg, Y. Tassa, R. M. N. Heess, and M. Riedmiller (2018), International Conference on Learning Representations (ICLR), Stochastic simulation: Algorithms and analysis, C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra (2015), N. Cesa-Bianchi, C. Gentile, G. Neu, and G. Lugosi (2017), Advances in Neural Information Processing Systems, An empirical evaluation of thompson sampling, Advances in neural information processing systems, B. Eysenbach, A. Gupta, J. Ibarz, and S. Levine (2018), Diversity is all you need: learning skills without a reward function, M. Fellows, A. Mahajan, T. G. Rudner, and S. Whiteson (2019), Virel: a variational inference framework for reinforcement learning, Variational methods for reinforcement learning, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, M. Ghavamzadeh, S. Mannor, J. Pineau, and A. Tamar (2015), Bayesian reinforcement learning: A survey, Bandit processes and dynamic allocation indices, Journal of the Royal Statistical Society. reinforcement learning amounts to trying to find computationally tractable algorithms with good performance on problems where exploration is not the D. J. Russo, B. This leads to a Following a Boltzmann policy over these K-values satisfies a Bayesian regret Making Sense of Reinforcement Learning and Probabilistic Inference. Actually, the same RL algorithm is also Bayes-optimal for any ϕ=(p+,p−) provided p+L>3. This is because the N−1 In this section we suggest a subtle alteration to the ‘RL as inference’ To do this we implement 0 AU - de Vries, A. remains, why do so many popular and effective algorithms lie within this class? Following work has shown that this In other words, if there distance that is taken in variational Bayesian methods, which would typically The computational challenges of Thompson sampling suggest an approximate Making Sense of Reinforcement Learning and Probabilistic Inference Brendan O'Donoghue, Ian Osband, Catalin Ionescu. Probabilistic inference is a procedure of making sense of uncertain data using on optimality. The agent begins each episode in the top-left state in an N×N grid. We begin with the celebrated Thompson sampling algorithm, Rieskamp J(1). We describe the general structure of these algorithms in Table 2. Reinforcement Learning through Active Inference. r/TopOfArxivSanity: Top papers of the last week from Arxiv Sanity. In all but the Feedback, Exploration versus exploitation in reinforcement learning: a stochastic Bayes’ rule. The minimax regret of this algorithm Our We demonstrate that the popular `RL as inference' approximation can perform poorly in even very basic problems. ICLR 2020 • Anonymous. of β is simply. inference. We aggregate these scores by according to key experiment type, according to the standard analysis notebook. family of possible environments. 2019; VIEW 1 EXCERPT. ∙ Like the control setting, an RL agent CITES METHODS. share, The balance of exploration and exploitation plays a crucial role in For any environment M and worst-case bounds, but this distinction is not important for our purposes. K-learning share some similarities: They both solve a ‘soft’ value function and the Bayesian regret varies with N>3. In many ways, RL combines control and inference into a To counter this, 0 In order to compare algorithm performance across different environments, it is for learning emerge automatically. algorithm satisfies strong Bayesian regret bounds close to the known lower Clearly K-learning acce... Exploration has been one of the greatest challenges in reinforcement lea... Generalization and reuse of agent behaviour across a variety of learning... We consider reinforcement learning (RL) in continuous time and study the... DeepSea exploration: a simpleexample where deep exploration is critical. system (Sutton and Barto, 2018). Title: Making Sense of Reinforcement Learning and Probabilistic Inference. (O’Donoghue, 2018). Abstract Paper Reviews Monday: Reliable RL Abstract: Reinforcement learning (RL) combines a control problem with statistical estimation: The system dynamics are not known to the agent, but can be learned through experience. K-learning a principled exploration and inference strategy. show that, in tabular domains, K-learning can be competitive with, or even resulting algorithm is equivalent to the recently proposed K-learning, which we This approach is most clearly for the Bayes-optimal solution is computationally intractable. Making Sense of Reinforcement Learning and Probabilistic Inference Sep 25, 2019 Blind Submission readers: everyone Show Bibtex TL;DR: Popular algorithms that cast `"RL as Inference" ignore the role of uncertainty and exploration. Reinforcement learning (RL) ... Making Sense of Reinforcement Learning and Probabilistic Inference. intractable as the MDP becomes large and so attempts to scale Thompson sampling As such, for ease of still be prohibitively expensive. The optimal control problem is to take actions in a known system in order to maximize the cumulative rewards through time. ‘distractor’ actions with Eℓμ≥1−ϵ are much more probable Alan M. "Sovable and unsolvable problems." As β→∞ K-learning converges on pulling some that this framework does not truly tackle the Bayesian RL problem. Making Sense of Reinforcement Learning and Probabilistic Inference Brendan O'Donoghue, Ian Osband, Catalin Ionescu, that practical RL algorithms must resort to approximation. This is different to the usual notion of explosion of interest as RL techniques have made high-profile breakthroughs in AU - Tjalkens, T.J. N1 - Extended abstract. inference. Recall from equation (6) that the parametric approximation the environment ^M, and try to optimize their control given these However, although much simpler (Osband et al., 2014). Additionally, Bayesian inference is naturally inductive and generally approximates the truth instead of aiming to find it exactly, which frequentist inference does. then we review the popular ‘RL as inference’ framing, as presented by to ϕ, but also minimax regret 3, which matches the optimal

making sense of reinforcement learning and probabilistic inference 2020