Constrained Reinforcement Learning with Smoothed Log Barrier Function

Understand

Reinforcement Learning (RL) has been widely applied to many control tasks and substantially improved the performances compared to conventional control methods in many domains where the reward function is well defined.

• However, for many real-world problems, it is often more convenient to formulate optimization problems in terms of rewards and constraints simultaneously.
• Optimizing such constrained problems via reward shaping can be difficult as it requires tedious manual tuning of reward functions with several interacting terms.
• Recent formulations which include constraints mostly require a pre-training phase, which often needs human expertise to collect data or assumes having a sub-optimal policy readily available.