This paper intends to bridge the gap between traditional and machine learning (ML) methods for dynamic portfolio optimization. We consider an investor who maximizes his utility from terminal wealth by dynamically allocating between risky and risk-free assets over time. Departing from the Deep Deterministic Policy Gradient (DDPG) algorithm by Lillicrap et al. (2016), we build a model-free reinforcement learning (RL) approach that is capable of deriving approximately optimal investment policies without any knowledge about the underlying market dynamics. It is agnostic to rebalancing frequency, allows for easy implementation of allocation constraints and requires low computational effort. For testing our algorithm we benchmark it against the theoretically optimal solution. Considering a realistic market model that is still theoretically feasible, enables us to show in detail the inherent connections between RL and dynamic portfolio optimization. Based on that we discuss the economic interpretability of this ML approach and the real-world applicability of such ML methods in general.
