  This article aims to combine factor investing and reinforcement learning
(RL). The agent learns through sequential random allocations which rely on
firms' characteristics. Using Dirichlet distributions as the driving policy, we
derive closed forms for the policy gradients and analytical properties of the
performance measure. This enables the implementation of REINFORCE methods,
which we perform on a large dataset of US equities. Across a large range of
parametric choices, our result indicates that RL-based portfolios are very
close to the equally-weighted (1/N) allocation. This implies that the agent
learns to be *agnostic* with regard to factors, which can partly be explained
by cross-sectional regressions showing a strong time variation in the
relationship between returns and firm characteristics.
