Relationship Explainable Multi-objective Reinforcement Learning with Semantic Explainability Generation

P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, and Y. Wu, “Openai baselines,” GitHub, GitHub repository , 2017

Original

1909

Earlier work this paper cites.

R. T. Rockafellar and R. J.-B. Wets, “Scenarios and policy aggregation in optimization under uncertainty,” Mathematics of Operations Research , vol. 16, no. 1, pp. 119–147, 1991

1991

Earlier work this paper cites.

Z. Wang, T. Schaul, M. Hessel, H. Van Hasselt, M. Lanctot, and N. De Freitas, “Dueling network architectures for deep reinforcement learning,” in Proceedings of the International Conference on International Conference on Machine Learning , 2016, pp. 1995–2003

2003

Earlier work this paper cites.

J. G. Lin, “On min-norm and min-max methods of multi-objective optimization,” Mathematical programming , vol. 103, no. 1, pp. 1–33, 2005

2005

Earlier work this paper cites.

A. Konak, D. W. Coit, and A. E. Smith, “Multi-objective optimization using genetic algorithms: A tutorial,” Reliability Engineering & System Safety , vol. 91, no. 9, pp. 992–1007, 2006

2006

Earlier work this paper cites.

G. Tesauro, R. Das, H. Chan, J. Kephart, D. Levine, F. Rawson, and C. Lefurgy, “Managing power consumption and performance of computing systems using reinforcement learning,” in Advances in Neural Information Processing Systems , 2008, pp. 1497–1504

2008

Earlier work this paper cites.

H. Nakayama, Y. Yun, and M. Yoon, Sequential approximate multiobjective optimization using computational intelligence . Springer Science & Business Media, 2009

2009

Earlier work this paper cites.

K. G. Vamvoudakis and F. L. Lewis, “Online actor–critic algorithm to solve the continuous-time infinite horizon optimal control problem,” Automatica , vol. 46, no. 5, pp. 878–888, 2010

2010

Earlier work this paper cites.

P. Vamplew, R. Dazeley, A. Berry, R. Issabekov, and E. Dekker, “Empirical evaluation methods for multiobjective reinforcement learning algorithms,” Machine Learning , vol. 84, no. 1-2, pp. 51–80, 2011

2011

Earlier work this paper cites.

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

2012

Earlier work this paper cites.

D. M. Roijers, P. Vamplew, S. Whiteson, and R. Dazeley, “A survey of multi-objective sequential decision-making,” Journal of Artificial Intelligence Research , vol. 48, pp. 67–113, 2013

2013

Earlier work this paper cites.

K. Van Moffaert, M. M. Drugan, and A. Nowé, “Scalarized multi-objective reinforcement learning: Novel design techniques.” in ADPRL , 2013, pp. 191–199

2013

Earlier work this paper cites.

X. Guo, S. Singh, H. Lee, R. L. Lewis, and X. Wang, “Deep learning for real-time atari game play using offline monte-carlo tree search planning,” in Advances in Neural Information Processing Systems , 2014, pp. 3338–3346

2014

Cited alongside, same era.

C. J. Maddison, A. Huang, I. Sutskever, and D. Silver, “Move evaluation in go using deep convolutional neural networks,” arXiv preprint arXiv:1412.6564 , 2014

Original

2014

Cited alongside, same era.

D. M. Roijers, J. Scharpff, M. T. Spaan, F. A. Oliehoek, M. De Weerdt, S. Whiteson, et al. , “Bounded approximations for linear multi-objective planning under uncertainty.” in International Conference on Automated Planning and Scheduling , 2014

2014

Cited alongside, same era.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

Original

2014

Cited alongside, same era.

H. Van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double q-learning.” in AAAI , vol. 2, 2016, p. 5

2016

Later among the works it cites.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. , “Mastering the game of go with deep neural networks and tree search,” Nature , vol. 529, no. 7587, p. 484, 2016

2016

Later among the works it cites.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al. , “Tensorflow: a system for large-scale machine learning.” in USENIX Symposium on Operating Systems Design and Implementation , vol. 16, 2016, pp. 265–283

2016

Later among the works it cites.

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

Later among the works it cites.