Xianxu Qiu

Evolutionary Reinforcement Learning with Parameterized Action Primitives for Diverse Manipulation Tasks

Reinforcement learning (RL) has shown promising performance in tackling robotic manipulation tasks (RMTs), which require learning a prolonged sequence of manipulation actions to control robots efficiently. However, most RL algorithms often suffer from two problems when solving RMTs: inefficient expl…