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Differentiable simulators promise faster computation time for reinforcement learning by replacing zeroth-order gradient estimates of a stochastic objective with an estimate based on first-order gradients.
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Stochastic first- and zeroth-order methods for nonconvex stochastic programming
Ghadimi, S. and Lan, G · 2013
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Unified particle physics for real-time applications
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Schulman, J., Heess, N., Weber, T., and Abbeel, P · 2015
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Du, T., Li, Y., Xu, J., Spielberg, A., Wu, K., Rus, D., and Matusik, W · 2020
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Add: Analytically differentiable dynamics for multi-body systems with frictional contact, 2020
Geilinger, M., Hahn, D., Zehnder, J., Bächer, M., Thomaszewski, B., and Coros, S · 2020
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Mahamed, S., Rosca, M., Figurnov, M., and Mnih, A · 2020
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Global convergence of policy gradient methods to (almost) locally optimal policies, 2020
Zhang, K., Koppel, A., Zhu, H., and Başar, T · 2020
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Systematically differentiating parametric discontinuities
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Metz, L., Freeman, C. D., Schoenholz, S. S., and Kachman, T · 2021
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Bundled gradients through contact via randomized smoothing
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Fast and feature-complete differentiable physics for articulated rigid bodies with contact, 2021
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Howell, T. A., Cleac’h, S. L., Kolter, J. Z., Schwager, M., and Manchester, Z · 2022
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Drake: A planning, control, and analysis toolbox for nonlinear dynamical systems, 2022
Tedrake, R · 2022
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