Researchers at UC San Diego Propose DrS: A Novel Machine Learning Approach for Learning Reusable Dense Rewards for Multi-Stage Tasks in a Data-Driven Manner
The success of many reinforcement learning (RL) techniques relies on dense reward functions, but designing them can be difficult due to expertise requirements and trial and error. Sparse rewards, like binary task completion signals, are […]