  Although Reinforcement Learning (RL) is effective for sequential
decision-making problems under uncertainty, it still fails to thrive in
real-world systems where risk or safety is a binding constraint. In this paper,
we formulate the RL problem with safety constraints as a non-zero-sum game.
While deployed with maximum entropy RL, this formulation leads to a safe
adversarially guided soft actor-critic framework, called SAAC. In SAAC, the
adversary aims to break the safety constraint while the RL agent aims to
maximize the constrained value function given the adversary's policy. The
safety constraint on the agent's value function manifests only as a repulsion
term between the agent's and the adversary's policies. Unlike previous
approaches, SAAC can address different safety criteria such as safe
exploration, mean-variance risk sensitivity, and CVaR-like coherent risk
sensitivity. We illustrate the design of the adversary for these constraints.
Then, in each of these variations, we show the agent differentiates itself from
the adversary's unsafe actions in addition to learning to solve the task.
Finally, for challenging continuous control tasks, we demonstrate that SAAC
achieves faster convergence, better efficiency, and fewer failures to satisfy
the safety constraints than risk-averse distributional RL and risk-neutral soft
actor-critic algorithms.
