A policy gradient approach for Finite Horizon Constrained Markov Decision Processes

Understand

The infinite horizon setting is widely adopted for problems of reinforcement learning (RL).

• These invariably result in stationary policies that are optimal.
• In many situations, finite horizon control problems are of interest and for such problems, the optimal policies are time-varying in general.
• Another setting that has become popular in recent times is of Constrained Reinforcement Learning, where the agent maximizes its rewards while it also aims to satisfy some given constraint criteria.