Kailin Zeng

APD: Learning Diverse Behaviors for Reinforcement Learning Through Unsupervised Active Pre-Training

Unsupervised pre-training in reinforcement learning enables the agent to gain prior environmental knowledge, which is then fine-tuned in the supervised stage to quickly adapt to various downstream tasks. In the absence of task-related rewards, pre-training aims to acquire policies (i.e., behaviors)