Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies A common strategy to deal with the expensive reinforcement learning (RL) of complex tasks is to decompose them into a collection of subtasks that are usually simpler to learn as well as reusable for new problems. However, when a robot learns the policies for these subtasks, common approaches treat every policy learning process separately. Therefore, all these individual (composable) policies need to be learned before tackling the learning process of the complex task through policies composition. Moreover, such composition of individual policies is usually performed sequentially, which is not suitable for tasks that require to perform the subtasks concurrently. In this paper, we propose to combine a set of composable Gaussian policies corresponding to these subtasks using a set of activation vectors, resulting in a complex Gaussian policy that is a function of the means and covariances matrices of the composable policies. Moreover, we propose an algorithm for learning both compound and composable policies within the same learning process by exploiting the offpolicy data generated from the compound policy. The algorithm is built on a maximum entropy RL approach to favor exploration during the learning process. The results of the experiments show that the experience collected with the compound policy permits not only to solve the complex task but also to obtain useful composable policies that successfully perform in their corresponding subtasks.

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.