Title: Variational Bayesian Reinforcement Learning with Regret Bounds Authors: Brendan O'Donoghue (Submitted on 25 Jul 2018 (this version), latest version 1 Jul 2019 ( v2 )) [1807.09647] Variational Bayesian Reinforcement Learning with Regret Bounds arXiv.org – Jul 25, 2018 Abstract: We consider the exploration-exploitation trade-off in reinforcement learning and we show that an agent imbued with a risk-seeking utility function is able to explore efficiently, as measured by regret. This policy achieves a Bayesian regret bound of $\tilde O(L^{3/2} \sqrt{SAT})$, where L is the time horizon, S is the number of states, A is the number of actions, and T is the total number of elapsed time-steps. Variational Bayesian RL with Regret Bounds ; Video Presentation. So far, variational regret bounds have been derived only for the simpler bandit setting (Besbes et al., 2014). my subreddits. Deep Residual Learning for Image Recognition. Stabilising Experience Replay for Deep Multi-Agent RL ; Counterfactual Multi-Agent Policy Gradients ; Value-Decomposition Networks For Cooperative Multi-Agent Learning ; Monotonic Value Function Factorisation for Deep Multi-Agent RL ; Multi-Agent Actor … World's Most Famous Hacker Kevin Mitnick & KnowBe4's Stu Sjouwerman Opening Keynote - Duration: 36:30. K-learning is simple to implement, as it only requires adding a bonus to the reward at each state-action and then solving a Bellman equation. Reddit. Brendan O'Donoghue, We consider the exploration-exploitation trade-off in reinforcement learning and we show that an agent imbued with an epistemic-risk-seeking utility function is able to explore efficiently, as measured by regret. Variational Regret Bounds for Reinforcement Learning. Lehrstuhl für Informationstechnologie; Details. Add a Variational Bayesian Reinforcement Learning with Regret Bounds. We call the resulting algorithm K-learning and we show that the K-values that the agent maintains are optimistic for the expected optimal Q-values at each state-action pair. (read more). K-learning is simple to implement, as it only requires adding a bonus to the reward at each state-action and then solving a Bellman equation. The state-of-the-art estimates the optimal action values while it usually involves an extensive search over the state-action space and unstable optimization. K-learning can be interpreted as mirror descent in the policy space, and it is similar to other well-known methods in the literature, including Q-learning, soft-Q-learning, and maximum entropy policy gradient. The K-values induce a natural Boltzmann exploration policy for which the `temperature' parameter is equal to the risk-seeking parameter. Variational Regret Bounds for Reinforcement Learning. We study a version of the classical zero-sum matrix game with unknown payoff matrix and bandit feedback, where the players only observe each others actions and a noisy payoff. Variational Regret Bounds for Reinforcement Learning. 25 Jul 2018 To date, Bayesian reinforcement learning has succeeded in learning observation and transition distributions (Jaulmes et al., 2005; ... We note however that the Hoeffding bounds used to derive this approximation are quite loose; for example in the shuttle POMDP problem, we used 200 samples, whereas equation 8 suggested over 3000 samples may have been necessary even with a perfect … Bibliographic details on Variational Bayesian Reinforcement Learning with Regret Bounds. Authors: Brendan O'Donoghue. K-learning can be interpreted as mirror descent in the policy space, and it is similar to other well-known methods in the literature, including Q-learning, soft-Q-learning, and maximum entropy policy gradient, and is closely related to optimism and count based exploration methods. Optimistic posterior sampling for reinforcement learning: worst-case regret bounds Shipra Agrawal Columbia University sa3305@columbia.edu Randy Jia Columbia University rqj2000@columbia.edu Abstract We present an algorithm based on posterior sampling (aka Thompson sampling) that achieves near-optimal worst-case regret bounds when the underlying Markov Decision Process (MDP) is … Variational Inference MPC for Bayesian Model-based Reinforcement Learning Masashi Okada Panasonic Corp., Japan okada.masashi001@jp.panasonic.com Tadahiro Taniguchi Ritsumeikan Univ. The parameter that controls how risk-seeking the agent is can be optimized to minimize regret, or annealed according to a schedule... Title: Variational Bayesian Reinforcement Learning with Regret Bounds. Read article More Like This. ∙ Google ∙ 0 ∙ share . Join Sparrho today to stay on top of science. Beitrag in 35th Conference on Uncertainty in Artificial Intelligence, Tel Aviv, Israel. This policy achieves a Bayesian regret bound of $\tilde O(L^{3/2} \sqrt{SAT})$, where L is the time horizon, S is the number of states, A is the number of actions, and T is the total number of elapsed time-steps. Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally... jump to content. Download PDF Abstract: We consider the exploration-exploitation trade-off in reinforcement learning and we show that an agent imbued with an epistemic-risk-seeking utility function is able to explore efficiently, as measured by regret. K-learning can be interpreted as mirror descent in the policy space, and it is similar to other well-known methods in the literature, including Q-learning, soft-Q-learning, and maximum entropy policy gradient. Ronald Ortner, Pratik Gajane, Peter Auer. Title: Variational Bayesian Reinforcement Learning with Regret Bounds. Copy URL Link. Pin to... Share. Publikationen: Konferenzbeitrag › Paper › Forschung › (peer-reviewed) Autoren. / Ortner, Ronald; Gajane, Pratik; Auer, Peter. Cyber Investing Summit Recommended for you Despite numerous applications, this problem has received relatively little attention. Sample inefficiency is a long-lasting problem in reinforcement learning (RL). Get the latest machine learning methods with code. K-learning is simple to implement, as it only requires adding a bonus to the reward at each state-action and then solving a Bellman equation. We call the resulting algorithm K-learning and show that the corresponding K-values are optimistic for the expected Q-values at each state-action pair. Motivation: Stein Variational Gradient Descent (SVGD) is a popular, non-parametric Bayesian Inference algorithm that’s been applied to Variational Inference, Reinforcement Learning, GANs, and much more. Towards the sample-efficient RL, we propose ranking policy gradient (RPG), a policy gradient method that learns the optimal rank of a set of discrete actions. Browse our catalogue of tasks and access state-of-the-art solutions. This policy achieves an expected regret bound of Õ (L3/2SAT‾‾‾‾√), where L is the time horizon, S is the number of states, A is the number of actions, and T is the total number of elapsed time-steps. Facebook. Ronald Ortner; Pratik Gajane; Peter Auer ; Organisationseinheiten. arXiv 2020, Stochastic Matrix Games with Bandit Feedback, Operator splitting for a homogeneous embedding of the monotone linear complementarity problem. We conclude with a numerical example demonstrating that K-learning is competitive with other state-of-the-art algorithms in practice. Co-authors Badr-Eddine Chérief-Abdellatif EmtiyazKhan Approximate Bayesian Inference team https : ==emtiyaz:github:io= Pierre Alquier, RIKEN AIP Regret bounds for online variational inference. 2019. We call the resulting algorithm K-learning and we show that the K-values that the agent maintains are optimistic for the expected optimal Q-values at each state-action pair. 1.3 Outline The rest of the article is structured as follows. 1.2 Related Work Browse our catalogue of tasks and access state-of-the-art solutions. We conclude with a numerical example demonstrating that K-learning is competitive with other state-of-the-art algorithms in practice. Brendan O'Donoghue, Tor Lattimore, et al. Title: Variational Bayesian Reinforcement Learning with Regret Bounds. Tip: you can also follow us on Twitter So far, variational regret bounds have been derived only for the simpler bandit setting (Besbes et al., 2014). • Google+. Authors: Brendan O'Donoghue (Submitted on 25 Jul 2018) Abstract: We consider the exploration-exploitation trade-off in reinforcement learning and we show that an agent imbued with a risk-seeking utility function is able to explore efficiently, as measured by regret. They are an alternative to other approaches for approximate Bayesian inference such as Markov chain Monte Carlo, the Laplace approximation, etc. The parameter that controls how risk-seeking the agent is can be optimized exactly, or annealed according to a schedule. 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