
Making Sense of Reinforcement Learning and Probabilistic Inference
Reinforcement learning (RL) combines a control problem with statistical ...
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Approximate Robust Control of Uncertain Dynamical Systems
This work studies the design of safe control policies for largescale no...
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Optimal Reinforcement Learning for Gaussian Systems
The explorationexploitation tradeoff is among the central challenges o...
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Bayesian Reinforcement Learning: A Survey
Bayesian methods for machine learning have been widely investigated, yie...
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Exploration versus exploitation in reinforcement learning: a stochastic control approach
We consider reinforcement learning (RL) in continuous time and study the...
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Bayesian Reinforcement Learning in Factored POMDPs
Bayesian approaches provide a principled solution to the explorationexp...
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Revised ProgressiveHedgingAlgorithm Based Twolayer Solution Scheme for Bayesian Reinforcement Learning
Stochastic control with both inherent random system noise and lack of kn...
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Dual Control for Approximate Bayesian Reinforcement Learning
Control of nonepisodic, finitehorizon dynamical systems with uncertain dynamics poses a tough and elementary case of the explorationexploitation tradeoff. Bayesian reinforcement learning, reasoning about the effect of actions and future observations, offers a principled solution, but is intractable. We review, then extend an old approximate approach from control theorywhere the problem is known as dual controlin the context of modern regression methods, specifically generalized linear regression. Experiments on simulated systems show that this framework offers a useful approximation to the intractable aspects of Bayesian RL, producing structured exploration strategies that differ from standard RL approaches. We provide simple examples for the use of this framework in (approximate) Gaussian process regression and feedforward neural networks for the control of exploration.
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