Empirical evaluations play an important role in machine learning. However, the usefulness of any evaluation depends on the empirical methodology employed. Designing good empirical methodologies is difficult in part because agents can overfit test evaluations and thereby obtain misleadingly high scores. We argue that reinforcement learning is particularly vulnerable to environment overfitting and propose as a remedy generalized methodologies, in which evaluations are based on multiple environments sampled from a distribution. In addition, we consider how to summarize performance when scores from different environments may not have commensurate values. Finally, we present proof-of-concept results demonstrating how these methodologies can validate an intuitively useful range-adaptive tile coding method.

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In {IEEE} Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), April 2011.

@InProceedings{ADPRL11-shimon, title={Protecting Against Evaluation Overfitting in Empirical Reinforcement Learning}, author={Shimon Whiteson and Brian Tanner and Matthew E. Taylor and Peter Stone}, booktitle={{IEEE} Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)}, month={April}, url="http://www.cs.utexas.edu/users/ai-lab?ADPRL11-shimon", year={2011} }