Learning Reach-Avoid Task with Reinforcement Learning: Vectorized Simulation and Benchmark

arXiv:2607.15935v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) has a longstanding tradition in addressing the reach-avoid task problem, especially for controlling robotic arms. While this task serves as a baseline environment within the research community, the ability of DRL to effectively learn the each-avoid task in complex and realistic scenarios beyond simplified and restricted tabletop settings remains uncertain. In this paper, we present, for the first time, a comprehens...