Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress

Manuel Lopes 1 Tobias Lang 2 Marc Toussaint 2 Pierre-Yves Oudeyer 1
1 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
2 MLR - Machine Learning and Robotics [Stuttgart]
Abstract : Formal exploration approaches in model-based reinforcement learning estimate the accuracy of the currently learned model without consideration of the empirical prediction error. For example, PAC-MDP approaches such as R-MAX base their model certainty on the amount of collected data, while Bayesian approaches assume a prior over the transition dynamics. We propose extensions to such approaches which drive exploration solely based on empirical estimates of the learner's accuracy and learning progress. We provide a "sanity check" theoretical analysis, discussing the behavior of our extensions in the standard stationary finite state-action case. We then provide experimental studies demonstrating the robustness of these exploration measures in cases of non-stationary environments or where original approaches are misled by wrong domain assumptions.
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Communication dans un congrès
Neural Information Processing Systems (NIPS), Dec 2012, Lake Tahoe, United States. 2012
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Manuel Lopes, Tobias Lang, Marc Toussaint, Pierre-Yves Oudeyer. Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress. Neural Information Processing Systems (NIPS), Dec 2012, Lake Tahoe, United States. 2012. 〈hal-00755248〉

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