  Reinforcement learning (RL) involves performing exploratory actions in an
unknown system. This can place a learning agent in dangerous and potentially
catastrophic system states. Current approaches for tackling safe learning in RL
simultaneously trade-off safe exploration and task fulfillment. In this paper,
we introduce a new generation of RL solvers that learn to minimise safety
violations while maximising the task reward to the extent that can be tolerated
by the safe policy. Our approach introduces a novel two-player framework for
safe RL called Distributive Exploration Safety Training Algorithm (DESTA). The
core of DESTA is a game between two adaptive agents: Safety Agent that is
delegated the task of minimising safety violations and Task Agent whose goal is
to maximise the environment reward. Specifically, Safety Agent can selectively
take control of the system at any given point to prevent safety violations
while Task Agent is free to execute its policy at any other states. This
framework enables Safety Agent to learn to take actions at certain states that
minimise future safety violations, both during training and testing time, while
Task Agent performs actions that maximise the task performance everywhere else.
Theoretically, we prove that DESTA converges to stable points enabling safety
violations of pretrained policies to be minimised. Empirically, we show DESTA's
ability to augment the safety of existing policies and secondly, construct safe
RL policies when the Task Agent and Safety Agent are trained concurrently. We
demonstrate DESTA's superior performance against leading RL methods in Lunar
Lander and Frozen Lake from OpenAI gym.

Machine Learning