On the Analysis of Model-Free Methods for the Linear Quadratic Regulator

Author:

Jin Ze-Yu,Schmitt Johann Michael,Wen Zai-WenORCID

Funder

nsfc

Publisher

Springer Science and Business Media LLC

Reference32 articles.

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3. Bhatnagar, S., Precup, D., Silver, D., Sutton, R.S., Maei, H.R., Szepesvári, C.: Convergent temporal-difference learning with arbitrary smooth function approximation. In: Advances in Neural Information Processing Systems, pp. 1204–1212 (2009)

4. Borkar, V.S., Meyn, S.P.: The ode method for convergence of stochastic approximation and reinforcement learning. SIAM J. Control Optim. 38(2), 447–469 (2000)

5. Bradtke, S.J.: Reinforcement learning applied to linear quadratic regulation. In: Advances in Neural Information Processing Systems, pp. 295–302 (1993)

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