  In this research, some of the issues that arise from the scalarization of the
multi-objective optimization problem in the Advantage Actor Critic (A2C)
reinforcement learning algorithm are investigated. The paper shows how a naive
scalarization can lead to gradients overlapping. Furthermore, the possibility
that the entropy regularization term can be a source of uncontrolled noise is
discussed. With respect to the above issues, a technique to avoid gradient
overlapping is proposed, while keeping the same loss formulation. Moreover, a
method to avoid the uncontrolled noise, by sampling the actions from
distributions with a desired minimum entropy, is investigated. Pilot
experiments have been carried out to show how the proposed method speeds up the
training. The proposed approach can be applied to any Advantage-based
Reinforcement Learning algorithm.
