  In this paper, we study reinforcement learning (RL) algorithms to solve
real-world decision problems with the objective of maximizing the long-term
reward as well as satisfying cumulative constraints. We propose a novel
first-order policy optimization method, Interior-point Policy Optimization
(IPO), which augments the objective with logarithmic barrier functions,
inspired by the interior-point method. Our proposed method is easy to implement
with performance guarantees and can handle general types of cumulative
multiconstraint settings. We conduct extensive evaluations to compare our
approach with state-of-the-art baselines. Our algorithm outperforms the
baseline algorithms, in terms of reward maximization and constraint
satisfaction.
