  We propose a model-free reinforcement learning architecture, called
distributed attentional actor architecture after conditional attention (DA6-X),
to provide better interpretability of conditional coordinated behaviors. The
underlying principle involves reusing the saliency vector, which represents the
conditional states of the environment, such as the global position of agents.
Hence, agents with DA6-X flexibility built into their policy exhibit superior
performance by considering the additional information in the conditional states
during the decision-making process. The effectiveness of the proposed method
was experimentally evaluated by comparing it with conventional methods in an
objects collection game. By visualizing the attention weights from DA6-X, we
confirmed that agents successfully learn situation-dependent coordinated
behaviors by correctly identifying various conditional states, leading to
improved interpretability of agents along with superior performance.
