IPO: Interior-point Policy Optimization under Constraints

Understand

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.