DeepMind Control Suite

01/02/2018

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The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents. The tasks are written in Python and powered by the MuJoCo physics engine, making them easy to use and modify. We include benchmarks for several learning algorithms. The Control Suite is publicly available at https://www.github.com/deepmind/dm_control . A video summary of all tasks is available at http://youtu.be/rAai4QzcYbs .

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DeepMind Control Suite

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DeepMind Control Suite

Related Research

dm_control: Software and Tasks for Continuous Control

Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning

Marathon Environments: Multi-Agent Continuous Control Benchmarks in a Modern Video Game Engine

Behaviour Suite for Reinforcement Learning

DERAIL: Diagnostic Environments for Reward And Imitation Learning

When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning

Brax – A Differentiable Physics Engine for Large Scale Rigid Body Simulation