Songlin Ruan

Geometric-Feature Representation Based Pre-Training Method for Reinforcement Learning of Peg-in-Hole Tasks

Recently, reinforcement learning (RL) is often used for learning the strategy of peg-in-hole tasks. However, traditional state representation of PiH RL might be either redundant or abstract, which leads to unnecessary learning steps and incompatibility with mathematical training optimization. To iss