Efficiently Learning Small Policies for Locomotion and Manipulation

K. O. Stanley, D. B. D’Ambrosio, and J. Gauci, “A hypercube-based encoding for evolving large-scale neural networks,” Artificial life , vol. 15, no. 2, pp. 185–212, 2009

2009

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.

G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” CoRR , vol. abs/1606.01540, 2016. [Online]. Available: http://arxiv.org/abs/1606.01540

Original

2016

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.” in ICLR (Poster) , 2016

2016

Earlier work this paper cites.

S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,” in 2017 IEEE international conference on robotics and automation (ICRA) . IEEE, 2017, pp. 3389–3396

2017

Earlier work this paper cites.

X. B. Peng, G. Berseth, K. Yin, and M. van de Panne, “Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning,” ACM Transactions on Graphics (Proc. SIGGRAPH 2017) , vol. 36, no. 4, 2017

2017

Earlier work this paper cites.

D. Ha, A. M. Dai, and Q. V. Le, “Hypernetworks,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net, 2017. [Online]. Available: https://openreview.net/forum?id=rkpACe1lx

2017

Earlier work this paper cites.

G. Friedland and M. M. Krell, “A capacity scaling law for artificial neural networks,” CoRR , vol. abs/1708.06019, 2017. [Online]. Available: http://arxiv.org/abs/1708.06019

Original

2017

Cited alongside, same era.

M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. Pieter Abbeel, and W. Zaremba, “Hindsight experience replay,” Advances in neural information processing systems , vol. 30, 2017

2017

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

2018

Cited alongside, same era.

C. Zhang, M. Ren, and R. Urtasun, “Graph hypernetworks for neural architecture search,” in International Conference on Learning Representations , 2018

2018

Cited alongside, same era.

T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,” Journal of Machine Learning Research , vol. 20, no. 55, pp. 1–21, 2019. [Online]. Available: http://jmlr.org/papers/v20/18-598.html

2019

Later among the works it cites.

B. Mazoure, T. Doan, A. Durand, J. Pineau, and R. D. Hjelm, “Leveraging exploration in off-policy algorithms via normalizing flows,” in Proceedings of the Conference on Robot Learning , ser. Proceedings of Machine Learning Research, L. P. Kaelbling, D. Kragic, and K. Sugiura, Eds., vol. 100. PMLR, 30 Oct–01 Nov 2020, pp. 430–444. [Online]. Available: https://proceedings.mlr.press/v100/mazoure20a.html

2020

Later among the works it cites.

Y. Song, M. Steinweg, E. Kaufmann, and D. Scaramuzza, “Autonomous drone racing with deep reinforcement learning,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 1205–1212

2021

Later among the works it cites.