On the Benefit of Optimal Transport for Curriculum Reinforcement Learning.

Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a tailored sequence of learning tasks, starting from easy ones and subsequently increasing their difficulty. Although the potential of curricula in RL has been clearly shown in various works, it is less clear how to generate them for a given learning environment, resulting in various methods aiming to automate this task. In this work, we focus on framing curricula as interpolations between task distributions, which has previously been shown to be a viable approach to CRL. Identifying key issues of existing methods, we frame the generation of a curriculum as a constrained optimal transport problem between task distributions. Benchmarks show that this way of curriculum generation can improve upon existing CRL methods, yielding high performance in various tasks with different characteristics.

Multi-Channel Interactive Reinforcement Learning for Sequential Tasks.

The ability to learn new tasks by sequencing already known skills is an important requirement for future robots. Reinforcement learning is a powerful tool for this as it allows for a robot to learn and improve on how to combine skills for sequential tasks. However, in real robotic applications, the cost of sample collection and exploration prevent the application of reinforcement learning for a variety of tasks. To overcome these limitations, human input during reinforcement can be beneficial to speed up learning, guide the exploration and prevent the choice of disastrous actions. Nevertheless, there is a lack of experimental evaluations of multi-channel interactive reinforcement learning systems solving robotic tasks with input from inexperienced human users, in particular for cases where human input might be partially wrong. Therefore, in this paper, we present an approach that incorporates multiple human input channels for interactive reinforcement learning in a unified framework and evaluate it on two robotic tasks with 20 inexperienced human subjects. To enable the robot to also handle potentially incorrect human input we incorporate a novel concept for self-confidence, which allows the robot to question human input after an initial learning phase. The second robotic task is specifically designed to investigate if this self-confidence can enable the robot to achieve learning progress even if the human input is partially incorrect. Further, we evaluate how humans react to suggestions of the robot, once the robot notices human input might be wrong. Our experimental evaluations show that our approach can successfully incorporate human input to accelerate the learning process in both robotic tasks even if it is partially wrong. However, not all humans were willing to accept the robot's suggestions or its questioning of their input, particularly if they do not understand the learning process and the reasons behind the robot's suggestions. We believe that the findings from this experimental evaluation can be beneficial for the future design of algorithms and interfaces of interactive reinforcement learning systems used by inexperienced users.