Emergency action termination for immediate reaction in hierarchical reinforcement learning

Hierarchical decomposition of control is unavoidable in large dynamical systems. In reinforcement learning (RL), it is usually solved with subgoals defined at higher policy levels and achieved at lower policy levels. Reaching these goals can take a substantial amount of time, during which it is not verified whether they are still worth pursuing. However, due to the randomness of the environment, these goals may become obsolete. In this paper, we address this gap in the state-of-the-art approaches and propose a method in which the validity of higher-level actions (thus lower-level goals) is constantly verified at the higher level. If the actions, i.e. lower Figure 1: Emergency action termination in hierarchical RL: level goals, become inadequate, they are replaced by more appropriate Left: Pursuing the long-term goal, the agent defines a shortterm ones. This way we combine the advantages of hierarchical one. Middle: When reaching the short-term goal, the RL, which is fast training, and flat RL, which is immediate reactivity.