\begin{center}
\textsc{Intelligent Inventory Control: \\Is Bootstrapping Worth Implementing?}

\author{Tatpong Katanyukul}
\affil{Faculty of Engineering, Khon Kaen University, Thailand}
\email{tatpong@kku.ac.th}

\author{Edwin K. P. Chong}
\affil{College of Engineering, Colorado State University, USA}
\email{edwin.chong@colostate.edu}

\author{William S. Duff}
\affil{College of Engineering, Colorado State University, USA}
\email{bill@engr.colostate.edu}

\end{center}

\begin{abstract}
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%This study investigates the application to an inventory problem of learning-based Approximate Dynamic Programming (ADP) approaches with and without bootstrapping techniques.
The common belief is that using Reinforcement Learning methods (RL) with bootstrapping gives better results than without. 
However, inclusion of bootstrapping increases the complexity of the RL implementation and requires significant effort. 
This study investigates whether inclusion of bootstrapping is worth the effort when applying RL to inventory problems.
Specifically, we investigate bootstrapping of the temporal difference learning method by using eligibility trace.
In addition, we develop a new bootstrapping extension to the Residual Gradient method to supplement our investigation.
The results show questionable benefit of bootstrapping when applied to inventory problems.
Significance tests could not confirm that bootstrapping had statistically significantly reduced costs of inventory controlled by a RL agent.
Our empirical results are based on a variety of problem settings, including demand correlations, demand variances, and cost structures.
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\end{abstract}

\begin{keywords}
reinforcement learning, approximate dynamic programming, bootstrapping, eligibility trace, intelligent agent, inventory control
\end{keywords}