Experimental Data for Multi-Task Deep RL Framework in Quadruped Robots

This dataset contains the experimental results and data related to the paper titled "A Multi-Task Deep Reinforcement Learning Framework Based on Curriculum Learning and Policy Distillation for Quadruped Robot Motor Skill Training." The study proposes a multi-task deep reinforcement learning framework based on curriculum learning and policy distillation, which aims to enhance the motor performance of quadruped robots across multiple continuous control tasks by leveraging knowledge from expert skill teachers