Built on

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Similar

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017
Original
2017
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E. Liang, R. Liaw, R. Nishihara, P. Moritz, R. Fox, K. Goldberg, J. Gonzalez, M. Jordan, and I. Stoica, “RLlib: Abstractions for distributed reinforcement learning,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. PMLR, 10–15 Jul 2018, pp. 3053–3062
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J. Achiam, “Spinning Up in Deep Reinforcement Learning,” 2018
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Then

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M. Körber, J. Lange, S. Rediske, S. Steinmann, and R. Glück, “Comparing popular simulation environments in the scope of robotics and reinforcement learning,” arXiv preprint arXiv:2103.04616 , 2021
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2021
Later among the works it cites.

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2022

skrl: Modular and Flexible Library for Reinforcement Learning

Serrano-Muñoz, Antonio, Chrysostomou, Dimitris, Bøgh, Simon et al.

Understand

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.

In addition to supporting environments that use the traditional interfaces from OpenAI Gym and DeepMind, it provides the facility to load, configure, and operate NVIDIA Isaac Gym and NVIDIA Omniverse Isaac Gym environments.
Furthermore, it enables the simultaneous training of several agents with customizable scopes (subsets of environments among all available ones), which may or may not share resources, in the same run.
The library's documentation can be found at https://skrl.readthedocs.io and its source code is available on GitHub at https://github.com/Toni-SM/skrl.

Built on

C. J. C. H. Watkins, “Learning from delayed rewards,” 1989
1989
Earlier work this paper cites.
G. A. Rummery and M. Niranjan, On-line Q-learning using connectionist systems . Citeseer, 1994, vol. 37
1994
Earlier work this paper cites.
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2006
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2012, pp. 5026–5033
2012
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602 , 2013
Original
2013
Earlier work this paper cites.
S. Tokui, K. Oono, S. Hido, and J. Clayton, “Chainer: a next-generation open source framework for deep learning,” in Proceedings of workshop on machine learning systems (LearningSys) in the twenty-ninth annual conference on neural information processing systems (NIPS) , vol. 5, 2015, pp. 1–6
2015
Earlier work this paper cites.
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971 , 2015
Original
2015
Earlier work this paper cites.
J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz, “Trust region policy optimization,” in International conference on machine learning . PMLR, 2015, pp. 1889–1897
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Earlier work this paper cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” arXiv preprint arXiv:1606.01540 , 2016
Original
2016
Earlier work this paper cites.
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Original
2016
Earlier work this paper cites.
H. Van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double q-learning,” in Proceedings of the AAAI conference on artificial intelligence , vol. 30, no. 1, 2016
2016
Earlier work this paper cites.

Similar

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017
Original
2017
Cited alongside, same era.
E. Liang, R. Liaw, R. Nishihara, P. Moritz, R. Fox, K. Goldberg, J. Gonzalez, M. Jordan, and I. Stoica, “RLlib: Abstractions for distributed reinforcement learning,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. PMLR, 10–15 Jul 2018, pp. 3053–3062
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Cited alongside, same era.
J. Achiam, “Spinning Up in Deep Reinforcement Learning,” 2018
2018
Cited alongside, same era.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International conference on machine learning . PMLR, 2018, pp. 1861–1870
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Cited alongside, same era.

Then

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Original
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Later among the works it cites.
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Original
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Original
2021
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Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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