On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability

The Journal of Artificial Intelligence Research

Vincent Francois-Lavet¹ 

关键词: reinforcement learning;    machine learning;    knowledge representation;   

DOI  :  10.1613/jair.1.11478

学科分类：人工智能

来源: Association for the Advancement of Artificial Intelligence

【 摘 要 】

This paper provides an analysis of the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data) in the context of reinforcement learning with partial observability. Our theoretical analysis formally characterizes that while potentially increasing the asymptotic bias, a smaller state representation decreases the risk of overfitting. This analysis relies on expressing the quality of a state representation by bounding $L_1$ error terms of the associated belief states. Theoretical results are empirically illustrated when the state representation is a truncated history of observations, both on synthetic POMDPs and on a large-scale POMDP in the context of smartgrids, with real-world data. Finally, similarly to known results in the fully observable setting, we also briefly discuss and empirically illustrate how using function approximators and adapting the discount factor may enhance the tradeoff between asymptotic bias and overfitting in the partially observable context.

【 授权许可】

CC BY