Composable Deep Reinforcement Learning for Robotic Manipulation

A. J. Ijspeert, J. Nakanishi, and S. Schaal, “Learning attractor landscapes for learning motor primitives,” in Advances in neural information processing systems , 2003, pp. 1547–1554

2003

Earlier work this paper cites.

H. J. Kappen, “Path integrals and symmetry breaking for optimal control theory,” Journal of Statistical Mechanics: Theory And Experiment , vol. 2005, no. 11, p. P11011, 2005

2005

Earlier work this paper cites.

J. Peters and S. Schaal, “Policy gradient methods for robotics,” in Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on . IEEE, 2006, pp. 2219–2225

2006

Earlier work this paper cites.

F. Guenter, M. Hersch, S. Calinon, and A. Billard, “Reinforcement learning for imitating constrained reaching movements,” Advanced Robotics , vol. 21, no. 13, pp. 1521–1544, 2007

2007

Earlier work this paper cites.

E. Todorov, “Linearly-solvable Markov decision problems,” in Advances in Neural Information Processing Systems . MIT Press, 2007, pp. 1369–1376

2007

Earlier work this paper cites.

B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey, “Maximum entropy inverse reinforcement learning,” in AAAI Conference on Artificial Intelligence , 2008, pp. 1433–1438

2008

Earlier work this paper cites.

J. Peters and S. Schall, “Reinforcement learning of motor skills with policy gradients,” Neural networks , vol. 21, no. 4, pp. 682–692, 2008

2008

Earlier work this paper cites.

E. Todorov, “General duality between optimal control and estimation,” in IEEE Conf. on Decision and Control . IEEE, 2008, pp. 4286–4292

2008

Earlier work this paper cites.

P. Pastor, H. Hoffmann, T. Asfour, and S. Schaal, “Learning and generalization of motor skills by learning from demonstration,” in Robotics and Automation, 2009. ICRA’09. IEEE International Conference on . IEEE, 2009, pp. 763–768

2009

Earlier work this paper cites.

M. Toussaint, “Robot trajectory optimization using approximate inference,” in Int. Conf. on Machine Learning . ACM, 2009, pp. 1049–1056

2009

Earlier work this paper cites.

E. Todorov, “Compositionality of optimal control laws,” in Advances in Neural Information Processing Systems , 2009, pp. 1856–1864

2009

Earlier work this paper cites.

E. Theodorou, J. Buchli, and S. Schaal, “Reinforcement learning of motor skills in high dimensions: A path integral approach,” in Robotics and Automation (ICRA), 2010 IEEE International Conference on . IEEE, 2010, pp. 2397–2403

2010

Earlier work this paper cites.

K. Rawlik, M. Toussaint, and S. Vijayakumar, “On stochastic optimal control and reinforcement learning by approximate inference,” Proceedings of Robotics: Science and Systems VIII , 2012

2012

Earlier work this paper cites.

E. Todorov, T. Erez, and Y. Tassa, “MuJoCo: A physics engine for model-based control,” in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on . IEEE, 2012, pp. 5026–5033

2012

Cited alongside, same era.

A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal, “Dynamical movement primitives: learning attractor models for motor behaviors,” Neural computation , vol. 25, no. 2, pp. 328–373, 2013

2013

Cited alongside, same era.

F. Zhang, J. Leitner, M. Milford, B. Upcroft, and P. Corke, “Towards vision-based deep reinforcement learning for robotic motion control,” arXiv preprint arXiv:1511.03791 , 2015

Original

2015

Cited alongside, same era.

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,” arXiv preprint arXiv:1509.02971 , 2015

Original

2015

Cited alongside, same era.

M. Večerík, T. Hester, J. Scholz, F. Wang, O. Pietquin, B. Piot, N. Heess, T. Rothörl, T. Lampe, and M. Riedmiller, “Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards,” arXiv preprint arXiv:1707.08817 , 2017

Original

2017

Later among the works it cites.

S. James, A. J. Davison, and E. Johns, “Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task,” arXiv preprint arXiv:1707.02267 , 2017

Original

2017

Later among the works it cites.

T. Haarnoja, H. Tang, P. Abbeel, and S. Levine, “Reinforcement learning with deep energy-based policies,” arXiv preprint arXiv:1702.08165 , 2017

Original

2017

Later among the works it cites.

A. Ghadirzadeh, A. Maki, D. Kragic, and M. Björkman, “Deep predictive policy training using reinforcement learning,” in Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on . IEEE, 2017, pp. 2351–2358

2017

Later among the works it cites.