Technical Note: \cal Q-Learning
Machine Learning
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Self-organizing maps
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Scenario Reduction Algorithms in Stochastic Programming
Computational Optimization and Applications
High-Performance Computing for Asset-Liability Management
Operations Research
Generating Scenario Trees for Multistage Decision Problems
Management Science
Application of reinforcement learning to the game of Othello
Computers and Operations Research
The Innovest Austrian Pension Fund Financial Planning Model InnoALM
Operations Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
Policy sharing between multiple mobile robots using decision trees
Information Sciences: an International Journal
From model-based control to data-driven control: Survey, classification and perspective
Information Sciences: an International Journal
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In this paper, we consider optimal consumption and strategic asset allocation decisions of an investor with a finite planning horizon. A Q-learning approach is used to maximize the expected utility of consumption. The first part of the paper presents conceptually the implementation of Q-learning in a discrete state-action space and illustrates the relation of the technique to the dynamic programming method for a simplified setting. In the second part of the paper, different generalization methods are explored and, compared to other implementations using neural networks, a combination with self-organizing maps (SOMs) is proposed. The resulting policy is compared to alternative strategies.