Importance mixing: Improving sample reuse in evolutionary policy search methods

Uhlenbeck, G. E., Ornstein, L. S., Sep 1930. On the theory of the brownian motion. Phys. Rev. 36, 823–841. URL \url

1930

Earlier work this paper cites.

Back, T., 1996. Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford university press

1996

Earlier work this paper cites.

Larrañaga, P., Lozano, J. A., 2001. Estimation of distribution algorithms: A new tool for evolutionary computation. Vol. 2. Springer Science & Business Media

2001

Earlier work this paper cites.

Wierstra, D., Schaul, T., Peters, J., Schmidhuber, J., 2008. Natural evolution strategies. In: IEEE Congress on Evolutionary Computation. IEEE, pp. 3381–3387

2008

Earlier work this paper cites.

Sun, Y., Wierstra, D., Schaul, T., Schmidhuber, J., 2009. Efficient natural evolution strategies. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation. ACM, pp. 539–546

2009

Earlier work this paper cites.

Akimoto, Y., Nagata, Y., Ono, I., Kobayashi, S., 2010. Bidirectional relation between cma evolution strategies and natural evolution strategies. In: International Conference on Parallel Problem Solving from Nature. Springer, pp. 154–163

2010

Earlier work this paper cites.

Glasmachers, T., Schaul, T., Yi, S., Wierstra, D., Schmidhuber, J., 2010. Exponential natural evolution strategies. In: Proceedings of the 12th annual conference on Genetic and evolutionary computation. ACM, pp. 393–400

2010

Earlier work this paper cites.

Arnold, L., Auger, A., Hansen, N., Ollivier, Y., 2011. Information-geometric optimization algorithms: A unifying picture via invariance principles. Tech. rep., INRIA Saclay

2011

Earlier work this paper cites.

Schaul, T., Glasmachers, T., Schmidhuber, J., 2011. High dimensions and heavy tails for natural evolution strategies. In: GECCO

2011

Cited alongside, same era.

Deisenroth, M. P., Neumann, G., Peters, J., et al., 2013. A survey on policy search for robotics. Foundations and Trends® in Robotics 2 (1–2), 1–142

2013

Cited alongside, same era.

Stulp, F., Sigaud, O., august 2013. Robot skill learning: From reinforcement learning to evolution strategies. Paladyn Journal of Behavioral Robotics 4 (1), 49–61

2013

Cited alongside, same era.

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

Original

2014

Cited alongside, same era.

Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D., 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971

Dhariwal, P., Hesse, C., Klimov, O., Nichol, A., Plappert, M., Radford, A., Schulman, J., Sidor, S., Wu, Y., 2017. OpenAI baselines. \url

2017

Later among the works it cites.

Petroski Such, F., Madhavan, V., Conti, E., Lehman, J., Stanley, K. O., Clune, J., 2017. Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint arXiv:1712.06567

Original

2017

Later among the works it cites.

Salimans, T., Ho, J., Chen, X., Sutskever, I., 2017. Evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint arXiv:1703.03864

Original

2017

Later among the works it cites.

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

Original

2017

Later among the works it cites.