DottoDot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation Robotic systems are ever more capable of automation and fulfilment of complex tasks, particularly with reliance on recent advances in intelligent systems, deep learning and artificial intelligence in general. However, as robots and humans come closer together in their interactions, the matter of interpretability, or explainability of robot decisionmaking processes for the human grows in importance. A successful interaction and collaboration would only be possible through mutual understanding of underlying representations of the environment and the task at hand. This is currently a challenge in deep learning systems. We present a hierarchical deep reinforcement learning system, consisting of a lowlevel agent handling the large actionsstates space of a robotic system efficiently, by following the directives of a highlevel agent which is learning the highlevel dynamics of the environment and task. This highlevel agent forms a representation of the world and task at hand that is interpretable for a human operator. The method, which we call DottoDot, is tested on a MuJoCobased model of the Fetch Robotics Manipulator, as well as a Shadow Hand, to test its performance. Results show efficient learning of complex actionsstates spaces by the lowlevel agent, and an interpretable representation of the task and decisionmaking process learned by the highlevel agent.

GraphBased Design of Hierarchical Reinforcement Learning Agents There is an increasing interest in Reinforcement Learning to solve new and more challenging problems, as those emerging in robotics and unmanned autonomous vehicles. To face these complex systems, a hierarchical and multiscale representation is crucial. This has brought the interest on Hierarchical Deep Reinforcement learning systems. Despite their successful application, Deep Reinforcement Learning systems suffer from a variety of drawbacks: they are data hungry, they lack of interpretability, and it is difficult to derive theoretical properties about their behavior. Classical Hierarchical Reinforcement Learning approaches, while not suffering from these drawbacks, are often suited for finite actions, and finite states, only. Furthermore, in most of the works, there is no systematic way to represent domain knowledge, which is often only embedded in the reward function.We present a novel Hierarchical Reinforcement Learning framework based on the hierarchical design approach typical of control theory. We developed our framework extending the block diagram representation of control systems to fit the needs of a Hierarchical Reinforcement Learning scenario, thus giving the possibility to integrate domain knowledge in an effective hierarchical architecture.