Autonomous Exploration and Mapping for Mobile Robots via Cumulative Curriculum Reinforcement Learning
Zhi Li,Jinghao Xin,Ning Li,Zhi Li,Jinghao Xin,Ning Li
Deep reinforcement learning (DRL) has been widely applied in autonomous exploration and mapping tasks, but often struggles with the challenges of sampling efficiency, poor adaptability to unknown map sizes, and slow simulation speed. To speed up convergence, we combine curriculum learning (CL) with DRL, and first propose a Cumulative Curriculum Reinforcement Learning (CCRL) training framework to a...
