In toy environments like video games, a reinforcement learning agent is
deployed and operates within the same state space in which it was
trained. However, in robotics applications such as industrial systems or
autonomous vehicles, this cannot be guaranteed. A robot can be pushed
out of its training space by some unforeseen perturbation, which may
cause it to go into an unknown state from which it has not been trained
to move towards its goal. While most prior work in the area of RL safety
focuses on ensuring safety in the training phase, this paper focuses on
ensuring the safe deployment of a robot that has already been trained to
operate within a safe space. This work defines a condition on the state
and action spaces, that if satisfied, guarantees the robot's recovery to
safety independently. We also propose a strategy and design that
facilitate this recovery within a finite number of steps after
perturbation. This is implemented and tested against a standard RL
model, and the results indicate a much-improved performance.