Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bottlenecks. This leads to blazing fast training times for complex robotics tasks on a single GPU with 2-3 orders of magnitude improvements compared to conventional RL training that uses a CPU based simulator and GPU for neural networks.
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T. Licht, L. Dohmen, P. Schmitz, L. Schmidt, и H. Luczak. Proceedings of the European Simulation and Modelling Conference (Paris 2004), стр. 188-195 *** Best Paper Award ***. Ghent, EUROSIS-ETI, (2004)