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. 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. However a very recent work (Agrawal & Jia,2017) have shown that an optimistic version of posterior sampling (us- Variational Bayesian Reinforcement Learning with Regret Bounds 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. Tip: you can also follow us on Twitter 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. Browse our catalogue of tasks and access state-of-the-art solutions. 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 … 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. Variational Bayesian Reinforcement Learning with Regret Bounds. Publikationen: Konferenzbeitrag › Paper › Forschung › (peer-reviewed) Autoren. Variational Regret Bounds for Reinforcement Learning. 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. Ronald Ortner; Pratik Gajane; Peter Auer ; Organisationseinheiten. / Ortner, Ronald; Gajane, Pratik; Auer, Peter. Join Sparrho today to stay on top of science. Minimax Regret Bounds for Reinforcement Learning benefits of such PSRL methods over existing optimistic ap-proaches (Osband et al.,2013;Osband & Van Roy,2016b) but they come with guarantees on the Bayesian regret only. In this survey, we provide an in-depth reviewof the role of Bayesian methods for the reinforcement learning RLparadigm. Despite numerous applications, this problem has received relatively little attention. 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. 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. 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. Authors: Brendan O'Donoghue. Beitrag in 35th Conference on Uncertainty in Artificial Intelligence, Tel Aviv, Israel. task. 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