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. 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. 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. 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. 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. 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. Variational Regret Bounds for Reinforcement Learning. Lehrstuhl für Informationstechnologie; Details. To the best of our knowledge, these bounds are the first variational bounds for the general reinforcement learning setting. Get the latest machine learning methods with code. [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. Variational Bayesian Reinforcement Learning with Regret Bounds. Authors: Brendan O'Donoghue. 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. Title: Variational Bayesian Reinforcement Learning with Regret Bounds. 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. The K-values induce a natural Boltzmann exploration policy for which the `temperature' parameter is equal to the risk-seeking parameter. 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 … 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 consider a Bayesian alternative that maintains a distribution over the tran-sition so that the resulting policy takes into account the limited experience of the envi- ronment. 1.3 Outline The rest of the article is structured as follows. Regret bounds for online variational inference Pierre Alquier ACML–Nagoya,Nov.18,2019 Pierre Alquier, RIKEN AIP Regret bounds for online variational inference. 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. Twitter. Facebook. 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 Regret Bounds for Reinforcement Learning. Variational Bayesian Reinforcement Learning with Regret Bounds 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.

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