Merge branch 'develop' of github.com:Shunichi-03/PythonLinearNonlinearControl into develop

README.md

@@ -17,8 +17,8 @@ Due to use only basic libralies (scipy, numpy), this library is easy to extend f
 |:----------|:---------------: |:----------------:|:----------------:|:----------------:|:----------------:|
 | Linear Model Predictive Control (MPC) | ✓ | x | x | x | x |
 | Cross Entropy Method (CEM) | ✓ | ✓ | x | x | x |
-| Model Predictive Path Integral Control of Nagabandi, A. (MPPI) | ✓ | ✓ | x | x | x |
-| Model Predictive Path Integral Control of Williams, G. (MPPIWilliams) | ✓ | ✓ | x | x | x |
+| Model Preidictive Path Integral Control of Nagabandi, A. (MPPI) | ✓ | ✓ | x | x | x |
+| Model Preidictive Path Integral Control of Williams, G. (MPPIWilliams) | ✓ | ✓ | x | x | x |
 | Random Shooting Method (Random) | ✓ | ✓ | x | x | x |
 | Iterative LQR (iLQR) | x | ✓ | x | ✓ | x |
 | Differential Dynamic Programming (DDP) | x | ✓ | x | ✓ | ✓ |
@@ -36,10 +36,10 @@ Following algorithms are implemented in PythonLinearNonlinearControl
 - [Cross Entropy Method (CEM)](https://arxiv.org/abs/1805.12114)
   - 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)
     - [script](PythonLinearNonlinearControl/controllers/cem.py)
-- [Model Predictive Path Integral Control of Nagabandi, A. (MPPI)](https://arxiv.org/abs/1909.11652)
+- [Model Preidictive Path Integral Control of Nagabandi, A. (MPPI)](https://arxiv.org/abs/1909.11652)
   - Ref: Nagabandi, A., Konoglie, K., Levine, S., & Kumar, V. (2019). Deep Dynamics Models for Learning Dexterous Manipulation. arXiv preprint arXiv:1909.11652.
     - [script](PythonLinearNonlinearControl/controllers/mppi.py)
-- [Model Predictive Path Integral Control of Williams, G. (MPPIWilliams)](https://ieeexplore.ieee.org/abstract/document/7989202)
+- [Model Preidictive Path Integral Control of Williams, G. (MPPIWilliams)](https://ieeexplore.ieee.org/abstract/document/7989202)
   - 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.
     - [script](PythonLinearNonlinearControl/controllers/mppi_williams.py)
 - [Random Shooting Method (Random)](https://arxiv.org/abs/1805.12114)
@@ -83,19 +83,25 @@ You could know abount our environmets more in [Environments.md](Environments.md)
 ## To install this package
 
 ```
-$ pip install PythonLinearNonlinearControl
+python setup.py install
+```
+
+or 
+
+```
+pip install .
 ```
 
 ## When developing the package
 
 ```
-$ python setup.py develop
+python setup.py develop
 ```
 
 or
 
 ```
-$ pip install -e .
+pip install -e .
 ```
 
 # Basic concepts

setup.py

@@ -13,7 +13,6 @@ setup(
     author_email='quick1st97of@gmail.com',
     install_requires=install_requires,
     url='https://github.com/Shunichi09/PythonLinearNonlinearControl',
-    license='MIT License',
     packages=find_packages(exclude=('tests', 'scripts')),
     setup_requires=setup_requires,
     test_suite='tests',