  Abstract -- Deep Actor-Critic algorithms, which combine Actor-Critic with
deep neural network (DNN), have been among the most prevalent reinforcement
learning algorithms for decision-making problems in simulated environments.
However, the existing deep Actor-Critic algorithms are still not mature to
solve realistic problems with non-convex stochastic constraints and high cost
to interact with the environment. In this paper, we propose a single-loop deep
Actor-Critic (SLDAC) algorithmic framework for general constrained
reinforcement learning (CRL) problems. In the actor step, the constrained
stochastic successive convex approximation (CSSCA) method is applied to handle
the non-convex stochastic objective and constraints. In the critic step, the
critic DNNs are only updated once or a few finite times for each iteration,
which simplifies the algorithm to a single-loop framework (the existing works
require a sufficient number of updates for the critic step to ensure a good
enough convergence of the inner loop for each iteration). Moreover, the
variance of the policy gradient estimation is reduced by reusing observations
from the old policy. The single-loop design and the observation reuse
effectively reduce the agent-environment interaction cost and computational
complexity. In spite of the biased policy gradient estimation incurred by the
single-loop design and observation reuse, we prove that the SLDAC with a
feasible initial point can converge to a Karush-Kuhn-Tuker (KKT) point of the
original problem almost surely. Simulations show that the SLDAC algorithm can
achieve superior performance with much lower interaction cost.
