A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control

Imagine you're developing a robot that needs to navigate various terrains and manipulate objects with precision. Current methods in reinforcement learning (RL) often rely heavily on predefined rules and demonstrations, which can be labor-intensive and limit the robot's adaptability. This is particularly problematic when scaling to more complex tasks, as the learning process can waste valuable experience on scenarios the robot has already mastered or cannot yet handle, leading to inefficiencies in training. This issue is known as experience waste, where the learning signal is diluted by irrelevant task configurations.

To address this, the authors propose a method called Success Guided Sampling (SGS), which intelligently focuses the training process on task configurations that are just beyond the robot's current capabilities. By concentrating on these challenging scenarios, SGS allows for more effective use of parallel simulations, enabling the robot to learn more efficiently across a vast number of environments. This approach not only improves the robot's performance in complex tasks like quadruped locomotion and assembly but also facilitates the transfer of learned skills to real-world applications, showcasing a practical advancement over previous methods that struggled with similar challenges.

The introduction of Success Guided Sampling offers a significant improvement in RL training efficiency.

The experiments demonstrate effective scaling and successful real-world application, though more baselines could strengthen claims.