  Portfolio optimization tasks describe sequential decision problems in which
the investor's wealth is distributed across a set of assets. Allocation
constraints are used to enforce minimal or maximal investments into particular
subsets of assets to control for objectives such as limiting the portfolio's
exposure to a certain sector due to environmental concerns. Although methods
for constrained Reinforcement Learning (CRL) can optimize policies while
considering allocation constraints, it can be observed that these general
methods yield suboptimal results. In this paper, we propose a novel approach to
handle allocation constraints based on a decomposition of the constraint action
space into a set of unconstrained allocation problems. In particular, we
examine this approach for the case of two constraints. For example, an investor
may wish to invest at least a certain percentage of the portfolio into green
technologies while limiting the investment in the fossil energy sector. We show
that the action space of the task is equivalent to the decomposed action space,
and introduce a new reinforcement learning (RL) approach CAOSD, which is built
on top of the decomposition. The experimental evaluation on real-world
Nasdaq-100 data demonstrates that our approach consistently outperforms
state-of-the-art CRL benchmarks for portfolio optimization.
