Posted by C. Daniel Freeman, Senior Software Engineer and Erik Frey, Staff Software Engineer, Google Research
Reinforcement learning (RL) is a popular method for teaching robots to navigate and manipulate the physical world, which itself can be simplified and expressed as interactions between rigid bodies1 (i.e., solid physical objects that do not deform when a force is applied to them). In order to facilitate the collection of training data in a practical amount of time, RL usually leverages simulation, where approximations of any number of complex objects are composed of many rigid bodies connected by joints and powered by actuators. But this poses a challenge: it frequently takes millions to billions of simulation frames for an RL agent to become proficient at even simple tasks, such as walking, using tools, or assembling toy blocks.
While progress has been made to improve training efficiency by recycling simulation frames, some RL tools instead sidestep this problem by distributing the generation of simulation frames across many simulators. These distributed simulation platforms yield impressive results that train very quickly, but they must run on compute clusters with thousands of CPUs or GPUs which are inaccessible to most researchers.
In “Brax – A Differentiable Physics Engine for Large Scale Rigid Body Simulation”, we present a new physics simulation engine that matches the performance of a large compute cluster with just a single TPU or GPU. The engine is designed to both efficiently run thousands of parallel physics simulations alongside a machine learning (ML) algorithm on a single accelerator and scale millions of simulations seamlessly across pods of interconnected accelerators. We’ve open sourced the engine along with reference RL algorithms and simulation environments that are all accessible via Colab. Using this new platform, we demonstrate 100-1000x faster training compared to a traditional workstation setup.
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