Reinforcement Learning with Automated Auxiliary Loss Search

Tairan He, Yuge Zhang, Kan Ren +5

A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative…