Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Empirical Evaluation of Interval Estimation for Markov Decision Processes
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Reinforcement learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Proceedings of the 24th international conference on Machine learning
Bayesian actor-critic algorithms
Proceedings of the 24th international conference on Machine learning
Uncertainty Propagation for Efficient Exploration in Reinforcement Learning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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In a typical reinforcement learning (RL) setting details of the environment are not given explicitly but have to be estimated from observations. Most RL approaches only optimize the expected value. However, if the number of observations is limited considering expected values only can lead to false conclusions. Instead, it is crucial to also account for the estimator's uncertainties. In this paper, we present a method to incorporate those uncertainties and propagate them to the conclusions. By being only approximate, the method is computationally feasible. Furthermore, we describe a Bayesian approach to design the estimators. Our experiments show that the method considerably increases the robustness of the derived policies compared to the standard approach.