Improving Safety in Reinforcement Learning Using Model-Based
  Architectures and Human Intervention
  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
