Title: Explainable Reinforcement Learning Through Goal-Based Interpretability. Abstract: Deep Reinforcement Learning agents achieve state-of-the-art performance in many tasks at the cost of making them black-boxes, hard to interpret and understand, making their use difficult in trusted applications, such as robotics or industrial applications. We introduce goal-based interpretability, where the agent produces goals which show the reason for its current actions (reach the current goal) and future goals indicate its desired future behavior without having to run the environment, a useful property in environments with no simulator. Additionally, in many environments, the goals can be visualized to make them easier to understand for non-experts. To have a goal-producing agent without requiring domain knowledge, we use 2-layer hierarchical agents where the top layer produces goals and the bottom layer attempts to reach those goals. 

Most classical reinforcement learning algorithms cannot be used train  goal-producing hierarchical agents. We introduce a new algorithm to train these more interpretable agents, called HAC-General with Teacher, an extension of the Hindsight Actor-Critic (HAC)  algorithm that adds 2 key improvements: (1) the goals now consist of a state $s$ to be reached and a reward $r$ to be collected, making it possible for the goal-producing policy to incentivize the goal-reaching policy to go through high-reward paths and (2) an expert teacher is leveraged to improve the training of the hierarchical agent, in a process similar but distinct to imitation learning and distillation. Contrarily to HAC, there is no requirement that environments need to provide the desired end state. Additionally, our experiments show that it has better performance and learns faster than HAC, and can solve environments that HAC fails to solve.