Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learning

Published:2020-10-16 Issue: Volume: Page:
ISSN:
Container-title:Motion, Interaction and Games
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Author:

Reda Daniele¹,Tao Tianxin¹,van de Panne Michiel¹

Affiliation:

1. University of British Columbia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3424636.3426907

Reference38 articles.

1. Farzad Abdolhosseini. 2019. Learning locomotion: symmetry and torque limit considerations. https://doi.org/10.14288/1.0383251 Farzad Abdolhosseini. 2019. Learning locomotion: symmetry and torque limit considerations. https://doi.org/10.14288/1.0383251

2. On Learning Symmetric Locomotion

3. Marcin Andrychowicz Anton Raichuk Piotr Stańczyk Manu Orsini Sertan Girgin Raphael Marinier Léonard Hussenot Matthieu Geist Olivier Pietquin Marcin Michalski 2020. What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. arXiv preprint arXiv:2006.05990(2020). Marcin Andrychowicz Anton Raichuk Piotr Stańczyk Manu Orsini Sertan Girgin Raphael Marinier Léonard Hussenot Matthieu Geist Olivier Pietquin Marcin Michalski 2020. What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. arXiv preprint arXiv:2006.05990(2020).

4. Curriculum learning

5. Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. arXiv:arXiv:1606.01540 Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. arXiv:arXiv:1606.01540

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