As per the different challenges in reinforcement learning algorithms, they cannot be directly implemented to robotics compared to supervised learning where large scale significant progress has already been done in terms of research and better deployment.
Reinforcement learning can be introduced for various physical systems and control tasks in robotics where risk isn't very high. The reason behind this is the question of stability of a reinforcement learning model in the real-world system. All learning processes require implemented domain knowledge for better state representations and devising accurate reward functions. This requires further research and development.
Let's discuss some of the open questions for reinforcement learning algorithms that require more attention in ongoing and future research in the space of robot reinforcement learning.
