A Q-Learning Approach to Derive Optimal Consumption and Investment Strategies
IEEE Transactions on Neural Networks, 2009, 20(8), 1234-1243
31 Pages Posted: 15 Feb 2008 Last revised: 1 Jul 2017
Date Written: February 12, 2008
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 relates the technique to the dynamic programming method. An illustrative example is given in 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 is proposed. The resulting, learned policy is compared to other strategies.
Keywords: Dynamic Programming, Reinforcement Learning, Q-learning, Self-organizing Maps, Finance, Asset Allocation
JEL Classification: C44, C45, G11
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