A Q-Learning Approach to Derive Optimal Consumption and Investment Strategies

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