Accelerating Reinforcement Learning with Learned Skill Priors

Understand

Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch.

• One approach for leveraging prior knowledge is to transfer skills learned on prior tasks to the new task.
• However, as the amount of prior experience increases, the number of transferable skills grows too, making it challenging to explore the full set of available skills during downstream learning.
• Yet, intuitively, not all skills should be explored with equal probability; for example information about the current state can hint which skills are promising to explore.