Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention
This work addresses safety in RL for real-world applications with human interaction, but it is incremental as it builds on existing hybrid methods.
The paper tackles the problem of scaling safe reinforcement learning by reducing human intervention time, achieving better sample efficiency and fewer catastrophic states compared to traditional model-free approaches.
Recent progress in AI and Reinforcement learning has shown great success in solving complex problems with high dimensional state spaces. However, most of these successes have been primarily in simulated environments where failure is of little or no consequence. Most real-world applications, however, require training solutions that are safe to operate as catastrophic failures are inadmissible especially when there is human interaction involved. Currently, Safe RL systems use human oversight during training and exploration in order to make sure the RL agent does not go into a catastrophic state. These methods require a large amount of human labor and it is very difficult to scale up. We present a hybrid method for reducing the human intervention time by combining model-based approaches and training a supervised learner to improve sample efficiency while also ensuring safety. We evaluate these methods on various grid-world environments using both standard and visual representations and show that our approach achieves better performance in terms of sample efficiency, number of catastrophic states reached as well as overall task performance compared to traditional model-free approaches