Work

Active exploration by searching for experiments that falsify the computed control policy

Year: 2011

Type: conference-paper

Abstract: We propose a strategy for experiment selection - in the context of reinforcement learning - based on the idea that the most interesting experiments to carry out at some stage are those that are the most liable to falsify the current hypothesis about the optimal control policy. We cast this idea in a context where a policy learning algorithm and a model identification method are given a priori. Experiments are selected if, using the learnt environment model, they are predicted to yield a revision of the learnt control policy. Algorithms and simulation results are provided for a deterministic system with discrete action space. They show that the proposed approach is promising.

Source: IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

DOI: 10.1109/adprl.2011.5967364

Authors: Raphael Fonteneau, Susan A. Murphy, Louis Wehenkel, Damien Ernst

Institutions: University of Liège, University of Michigan

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green