  Ensemble learning methods whose base classifier is a decision tree usually
belong to the bagging or boosting. However, no previous work has ever built the
ensemble classifier by maximizing long-term returns to the best of our
knowledge. This paper proposes a decision forest building method called
MA-H-SAC-DF for binary classification via deep reinforcement learning. First,
the building process is modeled as a decentralized partial observable Markov
decision process, and a set of cooperative agents jointly constructs all base
classifiers. Second, the global state and local observations are defined based
on informations of the parent node and the current location. Last, the
state-of-the-art deep reinforcement method Hybrid SAC is extended to a
multi-agent system under the CTDE architecture to find an optimal decision
forest building policy. The experiments indicate that MA-H-SAC-DF has the same
performance as random forest, Adaboost, and GBDT on balanced datasets and
outperforms them on imbalanced datasets.
