Isaac Gym Preview
2 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
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Libraries
Use these libraries to find Isaac Gym Preview models and implementationsMost implemented papers
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Denys88/rl_games
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Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU.
skrl: Modular and Flexible Library for Reinforcement Learning
toni-sm/skrl
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skrl is an open-source modular library for reinforcement learning written in Python and designed with a focus on readability, simplicity, and transparency of algorithm implementations.
