Alexander Mattick

SafeMPO: Constrained Reinforcement Learning with Probabilistic Incremental Improvement

Reinforcement Learning (RL) has demonstrated significant success in optimizing complex control and planning problems. However, scaling RL to real-world applications with multiple, potentially conflicting requirements requires an effective handling of constraints. We propose a novel approach to const…