POPO: Pessimistic Offline Policy Optimization

L.-J. Lin, “Self-improving reactive agents based on reinforcement learning, planning and teaching,” Machine learning , vol. 8, no. 3-4, pp. 293–321, 1992

1992

Earlier work this paper cites.

G. J. Gordon, “Stable function approximation in dynamic programming,” in Machine Learning Proceedings 1995 . Elsevier, 1995, pp. 261–268

1995

Earlier work this paper cites.

R. S. Sutton, D. A. McAllester, S. P. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” in Advances in neural information processing systems , 2000, pp. 1057–1063

2000

Earlier work this paper cites.

D. Ormoneit and Ś. Sen, “Kernel-based reinforcement learning,” Machine learning , vol. 49, no. 2-3, pp. 161–178, 2002

2002

Earlier work this paper cites.

D. Ernst, P. Geurts, and L. Wehenkel, “Tree-based batch mode reinforcement learning,” Journal of Machine Learning Research , vol. 6, no. Apr, pp. 503–556, 2005

2005

Earlier work this paper cites.

M. Riedmiller, “Neural fitted q iteration–first experiences with a data efficient neural reinforcement learning method,” in European Conference on Machine Learning . Springer, 2005, pp. 317–328

2005

Earlier work this paper cites.

L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of machine learning research , vol. 9, no. Nov, pp. 2579–2605, 2008

2008

Earlier work this paper cites.

A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” The Journal of Machine Learning Research , vol. 13, no. 1, pp. 723–773, 2012

2012

Earlier work this paper cites.

P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger, “Deep reinforcement learning that matters,” arXiv preprint arXiv:1709.06560 , 2017

Original

2012

Earlier work this paper cites.

B. Kim, A.-m. Farahmand, J. Pineau, and D. Precup, “Learning from limited demonstrations,” in Advances in Neural Information Processing Systems , 2013, pp. 2859–2867

2013

Earlier work this paper cites.

B. Piot, M. Geist, and O. Pietquin, “Boosted bellman residual minimization handling expert demonstrations,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2014, pp. 549–564

2014

Earlier work this paper cites.

D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller, “Deterministic policy gradient algorithms,” 2014

2014

Earlier work this paper cites.

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015

2015

Earlier work this paper cites.

J. Chemali and A. Lazaric, “Direct policy iteration with demonstrations,” in IJCAI-24th International Joint Conference on Artificial Intelligence , 2015

2015

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,” arXiv preprint arXiv:1509.02971 , 2015

Original

2015

Earlier work this paper cites.

O. Evans, A. Stuhlmüller, and N. D. Goodman, “Learning the preferences of ignorant, inconsistent agents,” arXiv preprint arXiv:1512.05832 , 2015

Original

2015

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016

Cited alongside, same era.

G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” arXiv preprint arXiv:1606.01540 , 2016

Original

2016

Cited alongside, same era.

J. Ho, J. Gupta, and S. Ermon, “Model-free imitation learning with policy optimization,” in International Conference on Machine Learning , 2016, pp. 2760–2769

2016

Cited alongside, same era.

H. Van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double q-learning,” in Thirtieth AAAI conference on artificial intelligence , 2016

2016

Cited alongside, same era.

O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev, et al. , “Grandmaster level in starcraft ii using multi-agent reinforcement learning,” Nature , vol. 575, no. 7782, pp. 350–354, 2019

2019

Later among the works it cites.

A. Kumar, J. Fu, M. Soh, G. Tucker, and S. Levine, “Stabilizing off-policy q-learning via bootstrapping error reduction,” in Advances in Neural Information Processing Systems , 2019, pp. 11 784–11 794

2019

Later among the works it cites.

R. Laroche, P. Trichelair, and R. T. Des Combes, “Safe policy improvement with baseline bootstrapping,” in International Conference on Machine Learning , 2019, pp. 3652–3661

2019

Later among the works it cites.

R. Agarwal, D. Schuurmans, and M. Norouzi, “Striving for simplicity in off-policy deep reinforcement learning,” arXiv preprint arXiv:1907.04543 , 2019

Original

2019

Later among the works it cites.