  In the trial-and-error mechanism of reinforcement learning (RL), a notorious
contradiction arises when we expect to learn a safe policy: how to learn a safe
policy without enough data and prior model about the dangerous region? Existing
methods mostly use the posterior penalty for dangerous actions, which means
that the agent is not penalized until experiencing danger. This fact causes
that the agent cannot learn a zero-violation policy even after convergence.
Otherwise, it would not receive any penalty and lose the knowledge about
danger. In this paper, we propose the safe set actor-critic (SSAC) algorithm,
which confines the policy update using safety-oriented energy functions, or the
safety indexes. The safety index is designed to increase rapidly for
potentially dangerous actions, which allows us to locate the safe set on the
action space, or the control safe set. Therefore, we can identify the dangerous
actions prior to taking them, and further obtain a zero constraint-violation
policy after convergence.We claim that we can learn the energy function in a
model-free manner similar to learning a value function. By using the energy
function transition as the constraint objective, we formulate a constrained RL
problem. We prove that our Lagrangian-based solutions make sure that the
learned policy will converge to the constrained optimum under some assumptions.
The proposed algorithm is evaluated on both the complex simulation environments
and a hardware-in-loop (HIL) experiment with a real controller from the
autonomous vehicle. Experimental results suggest that the converged policy in
all environments achieves zero constraint violation and comparable performance
with model-based baselines.
