Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)
Approximate Solutions of a Dynamic Forecast-Inventory Model
Manufacturing & Service Operations Management
A Robust Optimization Approach to Inventory Theory
Operations Research
Restricted gradient-descent algorithm for value-function approximation in reinforcement learning
Artificial Intelligence
Case-based myopic reinforcement learning for satisfying target service level in supply chain
Expert Systems with Applications: An International Journal
Value Function Based Reinforcement Learning in Changing Markovian Environments
The Journal of Machine Learning Research
A reinforcement learning model for supply chain ordering management: An application to the beer game
Decision Support Systems
Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system
Expert Systems with Applications: An International Journal
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Intelligent supply chain management using adaptive critic learning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This study investigates the application of learning-based and simulation-based Approximate Dynamic Programming (ADP) approaches to an inventory problem under the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. Specifically, we explore the robustness of a learning-based ADP method, Sarsa, with a GARCH(1,1) demand model, and provide empirical comparison between Sarsa and two simulation-based ADP methods: Rollout and Hindsight Optimization (HO). Our findings assuage a concern regarding the effect of GARCH(1,1) latent state variables on learning-based ADP and provide practical strategies to design an appropriate ADP method for inventory problems. In addition, we expose a relationship between ADP parameters and conservative behavior. Our empirical results are based on a variety of problem settings, including demand correlations, demand variances, and cost structures.