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

This research looks at the exploration bottleneck that appears when we use massive‑scale reinforcement learning (RL) for hard robot control problems, such as fast walking or precise hand manipulation. Running many simulations in parallel and resetting the simulator in many different ways is a practical alternative to hand‑crafting reward functions. However, if we sample uniformly from all possible resets, a lot of compute time is wasted on situations the policy already knows how to handle or on situations it cannot handle yet. To fix this, we study **Success Guided Sampling (SGS)**. SGS is an adaptive sampling method that concentrates training on the edge of what the policy can currently do.

It changes the sampling distribution on the fly, using the recent success rate to keep the learning signal strong in every training batch. The implementation support resource gives a step‑by‑step way to simulate, evaluate, and run the SGS algorithm in fast physics engines. The project measures: – how many samples are needed (sample efficiency) – how quickly the policy converges (convergence rate) – how well the learned policy transfers from simulation to real hardware (sim‑to‑real transfer) These tests are run on a variety of robot manipulation and locomotion benchmarks, providing a solid framework for academic work on scalable robot learning.

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