Efficient Uncertainty Propagation for Reinforcement Learning with Limited Data

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. Key words: reinforcement learning, model-based, uncertainty, Bayesian modeling

• Type: Conference
• Year: 2009
• Where: ICANN
• Authors: Alexander Hans, Steffen Udluft
• Keywords: Expected Values, ICANN 2009, Most RL, Neural Networks, Reinforcement Learning