An Agency-Transferring Model-Free Policy Enhancement Technique 事件

An Agency-Transferring Model-Free Policy Enhancement Technique arXiv:2606.09825v1 Announce Type: cross Abstract: Training reinforcement learning (RL) policies from scratch is costly: it requires careful reward and environment design, extensive tuning, and substantial computation. Yet many control problems already have a functional but suboptimal policy available as a baseline. This paper proposes a method for embedding such a baseline into the RL training process, simultaneously imp