MuJoCo stands for Multi-Joint dynamics with Contact. It is being developed by Emo Todorov for Roboti LLC. Initially it was used at the Movement Control Laboratory, University of Washington, and has now been adopted by a wide community of researchers and developers. MuJoCo is a physics engine aiming to facilitate research and development in robotics, biomechanics, graphics and animation, and other areas where fast and accurate simulation is needed. It offers a unique combination of speed, accuracy and modeling power, yet it is not merely a better simulator. Instead it is the first full-featured simulator designed from the ground up for the purpose of model-based optimization, and in particular optimization through contacts. MuJoCo makes it possible to scale up computationally-intensive techniques such optimal control, physically-consistent state estimation, system identification and automated mechanism design, and apply them to complex dynamical systems in contact-rich behaviors. It also has more traditional applications such as testing and validation of control schemes before deployment on physical robots, interactive scientific visualization, virtual environments, animation and gaming. Its key features are: ..

References in zbMATH (referenced in 26 articles )

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  1. Antonio Serrano-Muñoz, Nestor Arana-Arexolaleiba, Dimitrios Chrysostomou, Simon Bøgh: skrl: Modular and Flexible Library for Reinforcement Learning (2022) arXiv
  2. Kilinc, Ozsel; Montana, Giovanni: Reinforcement learning for robotic manipulation using simulated locomotion demonstrations (2022)
  3. Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres: PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance (2021) arXiv
  4. Cao, Yongcan; Zhan, Huixin: Efficient multi-objective reinforcement learning via multiple-gradient descent with iteratively discovered weight-vector sets (2021)
  5. D’eramo, Carlo; Tateo, Davide; Bonarini, Andrea; Restelli, Marcello; Peters, Jan: MushroomRL: simplifying reinforcement learning research (2021)
  6. Han, Minghao; Tian, Yuan; Zhang, Lixian; Wang, Jun; Pan, Wei: Reinforcement learning control of constrained dynamic systems with uniformly ultimate boundedness stability guarantee (2021)
  7. Hsu, Shao-Chen; Tadiparthi, Vaishnav; Bhattacharya, Raktim: A Lagrangian method for constrained dynamics in tensegrity systems with compressible bars (2021)
  8. Iwamoto, Masami; Kato, Daichi: Efficient actor-critic reinforcement learning with embodiment of muscle tone for posture stabilization of the human arm (2021)
  9. Klink, Pascal; Abdulsamad, Hany; Belousov, Boris; D’eramo, Carlo; Peters, Jan; Pajarinen, Joni: A probabilistic interpretation of self-paced learning with applications to reinforcement learning (2021)
  10. Krishnan, Srivatsan; Boroujerdian, Behzad; Fu, William; Faust, Aleksandra; Reddi, Vijay Janapa: Air learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation (2021)
  11. Lü, Shuai; Han, Shuai; Zhou, Wenbo; Zhang, Junwei: Recruitment-imitation mechanism for evolutionary reinforcement learning (2021)
  12. Ohnishi, Motoya; Notomista, Gennaro; Sugiyama, Masashi; Egerstedt, Magnus: Constraint learning for control tasks with limited duration barrier functions (2021)
  13. Bougie, Nicolas; Ichise, Ryutaro: Skill-based curiosity for intrinsically motivated reinforcement learning (2020)
  14. Ciosek, Kamil; Whiteson, Shimon: Expected policy gradients for reinforcement learning (2020)
  15. Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel: ProMP: Proximal Meta-Policy Search (2020) arXiv
  16. Lazaridis, Aristotelis; Fachantidis, Anestis; Vlahavas, Ioannis: Deep reinforcement learning: a state-of-the-art walkthrough (2020)
  17. Fan Fei, Zhan Tu, Yilun Yang, Jian Zhang, Xinyan Deng: Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals (2019) arXiv
  18. Parisi, Simone; Tangkaratt, Voot; Peters, Jan; Khan, Mohammad Emtiyaz: TD-regularized actor-critic methods (2019)
  19. Yasuhiro Fujita, Toshiki Kataoka, Prabhat Nagarajan, Takahiro Ishikawa: ChainerRL: A Deep Reinforcement Learning Library (2019) arXiv
  20. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)

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