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Commit eb0bf0c

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correct typos (#17)
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‎README.md‎

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@@ -17,8 +17,8 @@ Due to use only basic libralies (scipy, numpy), this library is easy to extend f
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|:----------|:---------------: |:----------------:|:----------------:|:----------------:|:----------------:|
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| Linear Model Predictive Control (MPC) || x | x | x | x |
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| Cross Entropy Method (CEM) ||| x | x | x |
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| Model Preidictive Path Integral Control of Nagabandi, A. (MPPI) ||| x | x | x |
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| Model Preidictive Path Integral Control of Williams, G. (MPPIWilliams) ||| x | x | x |
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| Model Predictive Path Integral Control of Nagabandi, A. (MPPI) ||| x | x | x |
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| Model Predictive Path Integral Control of Williams, G. (MPPIWilliams) ||| x | x | x |
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| Random Shooting Method (Random) ||| x | x | x |
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| Iterative LQR (iLQR) | x || x || x |
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| Differential Dynamic Programming (DDP) | x || x |||
@@ -36,10 +36,10 @@ Following algorithms are implemented in PythonLinearNonlinearControl
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- [Cross Entropy Method (CEM)](https://arxiv.org/abs/1805.12114)
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- Ref: Chua, K., Calandra, R., McAllister, R., & Levine, S. (2018). Deep reinforcement learning in a handful of trials using probabilistic dynamics models. In Advances in Neural Information Processing Systems (pp. 4754-4765)
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- [script](PythonLinearNonlinearControl/controllers/cem.py)
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- [Model Preidictive Path Integral Control of Nagabandi, A. (MPPI)](https://arxiv.org/abs/1909.11652)
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- [Model Predictive Path Integral Control of Nagabandi, A. (MPPI)](https://arxiv.org/abs/1909.11652)
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- Ref: Nagabandi, A., Konoglie, K., Levine, S., & Kumar, V. (2019). Deep Dynamics Models for Learning Dexterous Manipulation. arXiv preprint arXiv:1909.11652.
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- [script](PythonLinearNonlinearControl/controllers/mppi.py)
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- [Model Preidictive Path Integral Control of Williams, G. (MPPIWilliams)](https://ieeexplore.ieee.org/abstract/document/7989202)
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- [Model Predictive Path Integral Control of Williams, G. (MPPIWilliams)](https://ieeexplore.ieee.org/abstract/document/7989202)
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- Ref: Williams, G., Wagener, N., Goldfain, B., Drews, P., Rehg, J. M., Boots, B., & Theodorou, E. A. (2017, May). Information theoretic MPC for model-based reinforcement learning. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1714-1721). IEEE.
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- [script](PythonLinearNonlinearControl/controllers/mppi_williams.py)
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- [Random Shooting Method (Random)](https://arxiv.org/abs/1805.12114)

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