Robotic control in a continuous action space has long been a challenging topic. This is especially true when controlling robots to solve compound tasks, as both basic skills and compound skills need to be learned. In this paper, we propose a hierarchical deep reinforcement learning algorithm to learn basic skills and compound skills simultaneously. In the proposed algorithm, compound skills and basic skills are learned by two levels of hierarchy. In the first level of hierarchy, each basic skill is handled by its own actor, overseen by a shared basic critic. Then, in the second level of hierarchy, compound skills are learned by a meta critic by reusing basic skills. The proposed algorithm was evaluated on a Pioneer 3AT robot in three different navigation scenarios with fully observable tasks. The simulations were built in Gazebo 2 in a robot operating system Indigo environment. The results show that the proposed algorithm can learn both high performance basic skills and compound skills through the same learning process. The compound skills learned outperform those learned by a discrete action space deep reinforcement learning algorithm.

This paper considers two emerging interdisciplinary, but related topics that are likely to create tipping points in advancing the engineering and science areas. Trusted Autonomy (TA) is a field of research that focuses on understanding and designing the interaction space between two entities each of which exhibits a level of autonomy. These entities can be humans, machines, or a mix of the two. Cognitive Cyber Symbiosis (CoCyS) is a cloud that uses humans and machines for decision-making. In CoCyS, human–machine teams are viewed as a network with each node comprising humans (as computational machines) or computers. CoCyS focuses on the architecture and interface of a Trusted Autonomous System. This paper examines these two concepts and seeks to remove ambiguity by introducing formal definitions for these concepts. It then discusses open challenges for TA and CoCyS, that is, whether a team made of humans and machines can work in fluid, seamless harmony.

In this paper, we propose a deep reinforcement learning algorithm to learn multiple tasks concurrently. A new network architecture is proposed in the algorithm which reduces the number of parameters needed by more than 75% per task compared to typical single-task deep reinforcement learning algorithms. The proposed algorithm and network fuse images with sensor data and were tested with up to 12 movement-based control tasks on a simulated Pioneer 3AT robot equipped with a camera and range sensors. Results show that the proposed algorithm and network can learn skills that are as good as the skills learned by a comparable single-task learning algorithm. Results also show that learning performance is consistent even when the number of tasks and the number of constraints on the tasks increased.

A range of different value systems have been proposed for self-motivated agents, including biologically and cognitively inspired approaches. Likewise, these value systems have been integrated with different behavioral systems including reflexive architectures, reward-based learning and supervised learning. However, there is little literature comparing the performance of different value systems for motivating exploration and learning by robots. This paper proposes a neural network architecture for integrating different value systems with reinforcement learning. It then presents an empirical evaluation and comparison of four value systems for motivating exploration by a Lego Mindstorms NXT robot. Results reveal the different exploratory properties of novelty-seeking motivation, interest and competence-seeking motivation.

This correspondence describes an adaptation of puzzle-based learning to teaching an introductory computer programming course. Students from two offerings of the course-with and without the puzzle-based learning-were surveyed over a two-year period. Empirical results show that the synthesis of puzzle-based learning concepts with existing course content improves students' learning experience by increasing their interest and participation in the course and developing their critical thinking skills.