A Geometric Perspective on Self-Supervised Policy Adaptation

Understand

One of the most challenging aspects of real-world reinforcement learning (RL) is the multitude of unpredictable and ever-changing distractions that could divert an agent from what was tasked to do in its training environment.

• While an agent could learn from reward signals to ignore them, the complexity of the real-world can make rewards hard to acquire, or, at best, extremely sparse.
• A recent class of self-supervised methods have shown promise that reward-free adaptation under challenging distractions is possible.
• However, previous work focused on a short one-episode adaptation setting.