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Variance-reduced algorithms, although achieve great theoretical performance, can run slowly in practice due to the periodic gradient estimation with a large batch of data.
An improved convergence analysis of stochastic variance-reduced policy gradient
Xu, P., Gao, F., and Gu, Q · 1905
Earlier work this paper cites.
Sample efficient policy gradient methods with recursive variance reduction
Xu, P., Gao, F., and Gu, Q · 1909
Earlier work this paper cites.
A stochastic approximation method
Robbins, H. and Monro, S · 1951
Earlier work this paper cites.
Gradient methods for the minimisation of functionals
Polyak, B. T · 1963
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
S. Sutton, R., McAllester, D., Singh, S., and Mansour, Y · 2000
Earlier work this paper cites.
Infinite-horizon policy-gradient estimation
Baxter, J. and Bartlett, P. L · 2001
Earlier work this paper cites.
The optimal reward baseline for gradient-based reinforcement learning
Weaver, L. and Tao, N · 2001
Earlier work this paper cites.
Cubic regularization of newton method and its global performance
Nesterov, Y. and Polyak, B. T · 2006
Earlier work this paper cites.
Libsvm: a library for support vector machines
Chang, C.-C. and Lin, C.-J · 2011
Earlier work this paper cites.
Hybrid deterministic-stochastic methods for data fitting
Friedlander, M. P. and Schmidt, M · 2012
Earlier work this paper cites.
A stochastic gradient method with an exponential convergence rate for finite training sets
Roux, N. L., Schmidt, M., and Bach, F. R · 2012
Earlier work this paper cites.
Stochastic first-and zeroth-order methods for nonconvex stochastic programming
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A simple proximal stochastic gradient method for nonsmooth nonconvex optimization
Li, Z. and Li, J · 2018
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Stochastic variance-reduced policy gradient
Papini, M., Binaghi, D., Canonaco, G., Pirotta, M., and Restelli, M · 2018
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Don’t decay the learning rate, increase the batch size
Smith, S. L., Kindermans, P.-J., Ying, C., and Le, Q. V · 2018
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Policy optimization via stochastic recursive gradient algorithm
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On the adaptivity of stochastic gradient-based optimization
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