Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp
Junfan Lin,Zhongzhan Huang,Keze Wang,Xiaodan Liang,Weiwei Chen,Liang Lin,Junfan Lin,Zhongzhan Huang,Keze Wang,Xiaodan Liang,Weiwei Chen,Liang Lin
Although deep reinforcement learning (RL) has been successfully applied to a variety of robotic control tasks, it’s still challenging to apply it to real-world tasks, due to the poor sample efficiency. Attempting to overcome this shortcoming, several works focus on reusing the collected trajectory data during the training by decomposing them into a set of policy-irrelevant discrete transitions. Ho...
