Fetching the paper…
Reading the bibliography…
Deployment in hazardous environments requires robots to understand the risks associated with their actions and movements to prevent accidents.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “ Asynchronous Methods for Deep Reinforcement Learning ,” in Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016 , ser. JMLR Workshop and Conference Proceedings, vol. 48. JMLR.org, 2016, pp. 1928–1937
1937
Earlier work this paper cites.
S. S. Wang, “ A Class of Distortion Operators for Pricing Financial and Insurance Risks ,” The Journal of Risk and Insurance , vol. 67, no. 1, pp. 15–36, 2000
2000
Earlier work this paper cites.
R. T. Rockafellar and S. Uryasev, “ Optimization of conditional value-at risk ,” Journal of Risk , vol. 3, pp. 21–41, 2000
2000
Earlier work this paper cites.
2015
Earlier work this paper cites.
J. García, Fern, and o Fernández, “ A Comprehensive Survey on Safe Reinforcement Learning ,” Journal of Machine Learning Research , vol. 16, no. 42, pp. 1437–1480, 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Hutter, C. Gehring, D. Jud, A. Lauber, C. D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch, R. Diethelm, S. Bachmann, A. Melzer, and M. Hoepflinger, “ ANYmal - a highly mobile and dynamic quadrupedal robot ,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2016, pp. 38–44
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
H. Kolvenbach, M. Breitenstein, C. Gehring, and M. Hutter, “ Scalability Analysis of Legged Robots for Space Exploration .” 68th International Astronautical Congress (IAC), 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. G. Bellemare, W. Dabney, and R. Munos, “ A Distributional Perspective on Reinforcement Learning ,” in Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 , ser. Proceedings of Machine Learning Research, vol. 70. PMLR, 2017, pp. 449–458
2017
Earlier work this paper cites.
A. Majumdar and M. Pavone, “ How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics ,” in Robotics Research, The 18th International Symposium, ISRR 2017, Puerto Varas, Chile, December 11-14, 2017 , ser. Springer Proceedings in Advanced Robotics, vol. 10. Springer, 2017, pp. 75–84
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
G. Barth-Maron, M. W. Hoffman, D. Budden, W. Dabney, D. Horgan, D. TB, A. Muldal, N. Heess, and T. P. Lillicrap, “ Distributed Distributional Deterministic Policy Gradients ,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net, 2018
2018
Earlier work this paper cites.
G. Bledt, M. J. Powell, B. Katz, J. Di Carlo, P. M. Wensing, and S. Kim, “ MIT Cheetah 3: Design and Control of a Robust, Dynamic Quadruped Robot ,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 2245–2252
2018
Earlier work this paper cites.
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke, “ Sim-to-Real: Learning Agile Locomotion For Quadruped Robots ,” in Robotics: Science and Systems XIV, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA, June 26-30, 2018 , 2018
2018
Earlier work this paper cites.
W. Dabney, M. Rowland, M. G. Bellemare, and R. Munos, “ Distributional Reinforcement Learning With Quantile Regression ,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 . AAAI Press, 2018, pp. 2892–2901
2018
Earlier work this paper cites.
W. Dabney, G. Ostrovski, D. Silver, and R. Munos, “ Implicit Quantile Networks for Distributional Reinforcement Learning ,” in Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 , ser. Proceedings of Machine Learning Research, vol. 80. PMLR, 2018, pp. 1104–1113
2018
Earlier work this paper cites.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “ Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor ,” in Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 , ser. Proceedings of Machine Learning Research, vol. 80. PMLR, 2018, pp. 1856–1865
2018
Cited alongside, same era.
S. Fujimoto, H. van Hoof, and D. Meger, “ Addressing Function Approximation Error in Actor-Critic Methods. ” in ICML , ser. Proceedings of Machine Learning Research, vol. 80. PMLR, 2018, pp. 1582–1591
2018
Cited alongside, same era.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction , 2nd ed. The MIT Press, 2018
2018
Cited alongside, same era.
Y. C. Tang, J. Zhang, and R. Salakhutdinov, “ Worst Cases Policy Gradients ,” in 3rd Annual Conference on Robot Learning, CoRL 2019, Osaka, Japan, October 30 - November 1, 2019, Proceedings , ser. Proceedings of Machine Learning Research, vol. 100. PMLR, 2019, pp. 1078–1093
2021
Later among the works it cites.
D. W. Nam, Y. Kim, and C. Y. Park, “ GMAC: A Distributional Perspective on Actor-Critic Framework ,” in Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event , ser. Proceedings of Machine Learning Research, vol. 139. PMLR, 2021, pp. 7927–7936
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “ Learning agile and dynamic motor skills for legged robots ,” Science Robotics , vol. 4, no. 26, p. eaau5872, 2019
2019
Cited alongside, same era.
D. Yang, L. Zhao, Z. Lin, T. Qin, J. Bian, and T. Liu, “ Fully Parameterized Quantile Function for Distributional Reinforcement Learning ,” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , 2019, pp. 6190–6199
2019
Cited alongside, same era.
M. Rowland, R. Dadashi, S. Kumar, R. Munos, M. G. Bellemare, and W. Dabney, “ Statistics and Samples in Distributional Reinforcement Learning ,” in Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA , ser. Proceedings of Machine Learning Research, vol. 97. PMLR, 2019, pp. 5528–5536
2019
Cited alongside, same era.
2019
Cited alongside, same era.
J. Bernhard, S. Pollok, and A. Knoll, “ Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning ,” in 2019 IEEE Intelligent Vehicles Symposium (IV) . IEEE, jun 2019
2019
Cited alongside, same era.
S. Stanko and K. Macek, “ Risk-averse Distributional Reinforcement Learning: A CVaR Optimization Approach ,” in International Joint Conference on Computational Intelligence , 2019
2019
Cited alongside, same era.
2020
Cited alongside, same era.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “ Learning quadrupedal locomotion over challenging terrain ,” Science Robotics , vol. 5, no. 47, oct 2020
2020
Cited alongside, same era.
Later among the works it cites.
N. A. Urpí, S. Curi, and A. Krause, “ Risk-Averse Offline Reinforcement Learning ,” 2021
2021
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Dettmann, S. Planthaber, V. Bargsten, R. Dominguez, G. Cerilli, M. Marchitto, G. Fink, M. Focchi, V. Barasuol, C. Semini, and R. Marc, “ Towards a generic navigation and locomotion control system for legged space exploration .” 16th Symposium on Advanced Space Technologies in Robotics and Automation (ASTRA 2022), 2022
2022
Later among the works it cites.
T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “ Learning robust perceptive locomotion for quadrupedal robots in the wild ,” Science Robotics , vol. 7, no. 62, p. eabk2822, 2022
2022
Later among the works it cites.
A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “ Legged Locomotion in Challenging Terrains using Egocentric Vision ,” in Conference on Robot Learning, CoRL 2022, 14-18 December 2022, Auckland, New Zealand , ser. Proceedings of Machine Learning Research, vol. 205. PMLR, 2022, pp. 403–415
2022
Later among the works it cites.
R. Singh, K. Lee, and Y. Chen, “ Sample-based Distributional Policy Gradient ,” in Learning for Dynamics and Control Conference, L4DC 2022, 23-24 June 2022, Stanford University, Stanford, CA, USA , ser. Proceedings of Machine Learning Research, vol. 168. PMLR, 2022, pp. 676–688
2022
Later among the works it cites.
T. Théate and D. Ernst, “ Risk-Sensitive Policy with Distributional Reinforcement Learning ,” 2022
2022
Later among the works it cites.
A. Fawzi, M. Balog, A. Huang, T. Hubert, B. Romera-Paredes, M. Barekatain, A. Novikov, F. Ruiz, J. Schrittwieser, G. Swirszcz, D. Silver, D. Hassabis, and P. Kohli, “ Discovering faster matrix multiplication algorithms with reinforcement learning ,” Nature , vol. 610, pp. 47–53, 10 2022
2022
Later among the works it cites.
P. Wurman, S. Barrett, K. Kawamoto, J. MacGlashan, K. Subramanian, T. Walsh, R. Capobianco, A. Devlic, F. Eckert, F. Fuchs, L. Gilpin, P. Khandelwal, V. Kompella, H. Lin, P. MacAlpine, D. Oller, T. Seno, C. Sherstan, M. Thomure, and H. Kitano, “ Outracing champion Gran Turismo drivers with deep reinforcement learning ,” Nature , vol. 602, pp. 223–228, 02 2022
2022
Later among the works it cites.
T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter, “ Elevation Mapping for Locomotion and Navigation using GPU ,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, October 23-27, 2022 . IEEE, 2022, pp. 2273–2280
2022
Later among the works it cites.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.