The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Terrain-adaptive locomotion skills using deep reinforcement learning
Abstract
Reinforcement learning offers a promising methodology for developing skills for simulated characters, but typically requires working with sparse hand-crafted features. Building on recent progress in deep reinforcement learning (DeepRL), we introduce a mixture of actor-critic experts (MACE) approach that learns terrain-adaptive dynamic locomotion skills using high-dimensional state and terrain descriptions as input, and parameterized leaps or steps as output actions. MACE learns more quickly than a single actor-critic approach and results in actor-critic experts that exhibit specialization. Additional elements of our solution that contribute towards efficient learning include Boltzmann exploration and the use of initial actor biases to encourage specialization. Results are demonstrated for multiple planar characters and terrain classes.
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Cites
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
peng (2017) · doi:10.1145/3072959.3073602 · Cited by 502
Optimizing walking controllers
Wang, J. M., Fleet, D. J., & Hertzmann, A. (2009) · ACM Transactions on Graphics · doi:10.1145/1618452.1618514 · Cited by 181
Cited by
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Peng, X. B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460528 · Cited by 1,309
“The framework has enabled simulated agents to develop highly dynamic motor skills [4], [5], [6], [7].”
BackgroundSelf-citationSim-to-Real: Learning Agile Locomotion For Quadruped Robots
Tan, J., Zhang, T., Coumans, E., Iscen, A., Bai, Y., Hafner, D., Bohez, S., & Vanhoucke, V. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.010 · Cited by 735
Emergence of Locomotion Behaviours in Rich Environments
Heess, N., TB, D., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, S. M. A., et al. (2017) · arXiv · doi:10.48550/arxiv.1707.02286 · Cited by 657
peng (2017) · doi:10.1145/3072959.3073602 · Cited by 502
Learning to Walk Via Deep Reinforcement Learning
Haarnoja, T., Ha, S., Zhou, A., Tan, J., Tucker, G., & Levine, S. (2019) · Robotics: Science and Systems XV · doi:10.15607/rss.2019.xv.011 · Cited by 430
Metadata
- Authors
- Peng, X. B., Berseth, G., & van de Panne, M.
- Venue
- ACM Transactions on Graphics
- Published
- 2016-07-11
- Volume
- 35
- Issue
- 4
- Pages
- 1-12
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2460299708
- Cited by
- 200
- References
- 3
- FWCI
- 21.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Peng, X. B., Berseth, G., & van de Panne, M. (2016). Terrain-adaptive locomotion skills using deep reinforcement learning. ACM Transactions on Graphics. https://doi.org/10.1145/2897824.2925881
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