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2006
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An analytic solution to discrete Bayesian reinforcement learning

12 years 6 months ago
An analytic solution to discrete Bayesian reinforcement learning
Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms come short of achieving this goal because the amount of exploration required is often too costly and/or too time consuming for online learning. As a result, RL is mostly used for offline learning in simulated environments. We propose a new algorithm, called BEETLE, for effective online learning that is computationally efficient while minimizing the amount of exploration. We take a Bayesian model-based approach, framing RL as a partially observable Markov decision process. Our two main contributions are the analytical derivation that the optimal value function is the upper envelope of a set of multivariate polynomials, and an efficient pointbased value iteration algorithm that exploits this simple parameterization.
Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevi
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2006
Where ICML
Authors Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevin Regan
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