Guided Reinforcement Learning � A Review and Evaluation for Efficient and Effective Real-World Robotics

Recent successes aside, reinforcement learning still faces significant challenges in its application to the real-world robotics domain. Guiding the learning process with additional knowledge offers a potential solution, thus leveraging the strengths of data- and knowledge-driven approaches. However, this field of research encompasses several disciplines and hence would benefit from a structured overview. In this paper, we propose the concept of guided reinforcement learning that provides a systematic approach towards accel- erating the training process and improving the performance for real-world robotic settings. We introduce a taxonomy that structures guided reinforcement learning approaches and shows how different sources of knowledge can be integrated into the learning pipeline in a practical way. Based upon this, we describe available approaches in this field and quantitatively evaluate their specific impact in terms of efficiency, effective- ness, and sim-to-real transfer within the robotics domain.