[Reinforcement learning 🤖] MuJoCo physical simulator is free! (DeepMind)

#DeepReinforcementLearning #PhysicsSimulator #ReinforcementLearning

In case you missed the announcement from DeepMind on 18 October 2021: Opening up a physics simulator for robotics | DeepMind

The rich-yet-efficient contact model of the MuJoCo physics simulator has made it a leading choice by robotics researchers and today, we’re proud to announce that, as part of DeepMind’s mission of advancing science, we’ve acquired MuJoCo and are making it freely available for everyone, to support research everywhere. Already widely used within the robotics community, including as the physics simulator of choice for DeepMind’s robotics team, MuJoCo features a rich contact model, powerful scene description language, and a well-designed API. Together with the community, we will continue to improve MuJoCo as open-source software under a permissive licence. As we work to prepare the codebase, we are making MuJoCo freely available as a precompiled library.

MuJoCo in DeepMind. Our robotics team has been using MuJoCo as a simulation platform for various projects, mostly via our dm_control Python stack. In the carousel below, we highlight a few examples to showcase what can be simulated in MuJoCo. Of course, these clips represent only a tiny fraction of the vast possibilities for how researchers might use the simulator. For higher quality versions of these clips, please click here.

Website: https://mujoco.org/
Docs: Overview — MuJoCo documentation
GitHub: GitHub - deepmind/mujoco: Multi-Joint dynamics with Contact. A general purpose physics simulator.
Related - dm_control (for physics-based simulation): GitHub - deepmind/dm_control: DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

Related deep reinforcement learning research: Emergence of Locomotion Behaviours in Rich Environments (DeepMind):

… Benchmark tasks: We consider three continuous control tasks for benchmarking the algorithms. All environments rely on the Mujoco physics engine…

Demo of Emergence of Locomotion Behaviours in Rich Environments:

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