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Papers

DeepMind Control Suite

2018-01-02 · Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, Martin Riedmiller

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 .

📄 PDF Abstract BibTeX arXiv:1801.00690

Code (8)

deepmind/dm_control 공식 구현
NervanaSystems/coach tf
google-research/pisac tf
lqnew/continuous_control_benchmark
nicklashansen/tdmpc2 pytorch
ramanans1/dm_control
svikramank/dm_control
toni-sm/skrl jax

Tasks

continuous-controlContinuous ControlMuJoCoreinforcement-learningReinforcement LearningReinforcement Learning (RL)

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