Hybrid Genetic Programming and Deep Reinforcement Learning for Low-complexity Robot Arm Trajectory Planning

by Quentin Vacher (IETR VAADER) - 20YY.MM.DD

tags: VAADER Seminar

Abstract

This talk presents a hybrid approach to robot arm trajectory planning that combines Tangled Program Graphs (TPGs) with the Soft Actor-Critic (SAC) algorithm. While TPGs alone reduce computational complexity and model size, they struggle with more complex tasks. The hybrid solution leverages the strengths of both methods and achieves superior performance compared to state-of-the-art deep reinforcement learning algorithms, with execution times 6 to 20 times faster.