Understanding the Complexity Gains of Single-Task RL with a Curriculum

Understand

Reinforcement learning (RL) problems can be challenging without well-shaped rewards.

• Prior work on provably efficient RL methods generally proposes to address this issue with dedicated exploration strategies.
• However, another way to tackle this challenge is to reformulate it as a multi-task RL problem, where the task space contains not only the challenging task of interest but also easier tasks that implicitly function as a curriculum.
• Such a reformulation opens up the possibility of running existing multi-task RL methods as a more efficient alternative to solving a single challenging task from scratch.