RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration

D. Pomerleau, “Efficient Training of Artificial Neural Networks for Autonomous Navigation,” Neural Computation , 1991

1991

Earlier work this paper cites.

S. Schaal, “Learning from demonstration,” in Advances in neural information processing systems , 1997, pp. 1040–1046

1997

Earlier work this paper cites.

M. Pelikan, D. E. Goldberg, and E. Cantú-Paz, “Boa: The bayesian optimization algorithm,” in Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation-Volume 1 . Morgan Kaufmann Publishers Inc., 1999, pp. 525–532

1999

Earlier work this paper cites.

N. Hansen, S. D. Müller, and P. Koumoutsakos, “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es),” Evol. Comput. , vol. 11, no. 1, pp. 1–18, Mar. 2003

2003

Earlier work this paper cites.

D. A. Bristow, M. Tharayil, and A. G. Alleyne, “A survey of iterative learning control,” IEEE control systems magazine , vol. 26, no. 3, pp. 96–114, 2006

2006

Earlier work this paper cites.

J. Bödecker and M. Asada, “Simspark – concepts and application in the robocup 3 d soccer simulation league,” 2008

2008

Earlier work this paper cites.

J. Boedecker and M. Asada, “Simspark–concepts and application in the robocup 3d soccer simulation league,” in SIMPAR-2008 Workshop on the Universe of RoboCup Simulators , 2008, pp. 174–181

2008

Earlier work this paper cites.

B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration,” Robotics and autonomous systems , vol. 57, no. 5, pp. 469–483, 2009

2009

Earlier work this paper cites.

M. E. Taylor, H. B. Suay, and S. Chernova, “Integrating reinforcement learning with human demonstrations of varying ability,” in The 10th International Conference on Autonomous Agents and Multiagent Systems-Volume 2 , 2011, pp. 617–624

2011

Earlier work this paper cites.

M. Leonetti, P. Kormushev, and S. Sagratella, “Combining local and global direct derivative-free optimization for reinforcement learning,” Cybernetics and Information Technologies , vol. 12, no. 3, pp. 53–65, 2012

2012

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, IROS 2012, Vilamoura, Algarve, Portugal, October 7-12, 2012 . IEEE, 2012, pp. 5026–5033

2012

Earlier work this paper cites.

Y. Tassa, T. Erez, and E. Todorov, “Synthesis and stabilization of complex behaviors through online trajectory optimization,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2012, pp. 4906–4913

2012

Earlier work this paper cites.

J. Hwangbo, C. Gehring, H. Sommer, R. Siegwart, and J. Buchli, “Rock∗—efficient black-box optimization for policy learning,” in 2014 IEEE-RAS International Conference on Humanoid Robots . IEEE, 2014, pp. 535–540

2014

Earlier work this paper cites.

Y. Xu and H. Vatankhah, “Simspark: An open source robot simulator developed by the robocup community,” in RoboCup 2013: Robot World Cup XVII , S. Behnke, M. Veloso, A. Visser, and R. Xiong, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014, pp. 632–639

2014

Earlier work this paper cites.

Y. Xu and H. Vatankhah, “Simspark: An open source robot simulator developed by the robocup community,” in RoboCup 2013: Robot World Cup XVII . Springer, 2014, pp. 632–639

2014

Cited alongside, same era.

A. Marco Valle, “Gaussian process optimization for self-tuning control,” Master’s thesis, Universitat Politècnica de Catalunya, 2015

2015

Cited alongside, same era.

J. Schulman, S. Levine, P. Moritz, M. I. Jordan, and P. Abbeel, “Trust region policy optimization,” CoRR , vol. abs/1502.05477, 2015

Original

2015

Cited alongside, same era.

A. S. Lakshminarayanan, S. Ozair, and Y. Bengio, “Reinforcement learning with few expert demonstrations,” in NIPS Workshop on Deep Learning for Action and Interaction , vol. 2016, 2016

2016

Cited alongside, same era.

J. Ho and S. Ermon, “Generative adversarial imitation learning,” in NIPS , 2016, pp. 4565–4573

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017

Original

2017

Later among the works it cites.

L. P. Reis, N. Lau, A. Abdolmaleki, N. Shafii, R. Ferreira, A. Pereira, and D. Simões, “Fc portugal 3d simulation team: Team description paper 2017,” in RoboCup Symposium , 2017

2017

Later among the works it cites.

T. Iwanaga, K. Onda, and T. Yamanishi, “Fut-k team description paper 2017.”

2017

Later among the works it cites.

P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger, “Deep reinforcement learning that matters,” CoRR , vol. abs/1709.06560, 2017

Original

2017

Later among the works it cites.

R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction . MIT press, 2018

2018