Update README.md

Author: krzjoa

README.md

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 ## Reinforcement Learning
 * [OpenAI Gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.
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-## Distributed Computing
-* [Horovod](https://github.com/uber/horovod) - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [PySpark](https://spark.apache.org/docs/0.9.0/python-programming-guide.html) - Exposes the Spark programming model to Python. <img height="20" src="img/spark_big.png" alt="Apache Spark based">
-* [Veles](https://github.com/Samsung/veles) - Distributed machine learning platform.
-* [Jubatus](https://github.com/jubatus/jubatus) - Framework and Library for Distributed Online Machine Learning.
-* [DMTK](https://github.com/Microsoft/DMTK) - Microsoft Distributed Machine Learning Toolkit.
-* [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning.
-* [dask-ml](https://github.com/dask/dask-ml) - Distributed and parallel machine learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [Distributed](https://github.com/dask/distributed) - Distributed computation in Python.
-=======
 * [Coach](https://github.com/NervanaSystems/coach) - Easy experimentation with state of the art Reinforcement Learning algorithms.
 * [garage](https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.
 * [OpenAI Baselines](https://github.com/openai/baselines) - High-quality implementations of reinforcement learning algorithms.
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 * [TF-Agents](https://github.com/tensorflow/agents) - A library for Reinforcement Learning in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn"> 
 * [TensorForce](https://github.com/reinforceio/tensorforce) - A TensorFlow library for applied reinforcement learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [TRFL](https://github.com/deepmind/trfl) - TensorFlow Reinforcement Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [Dopamine](https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement learning algorithms.
+* [Dopamine](https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement learning algorithms. 
 * [keras-rl](https://github.com/keras-rl/keras-rl) - Deep Reinforcement Learning for Keras. <img height="20" src="img/keras_big.png" alt="Keras compatible">
 * [ChainerRL](https://github.com/chainer/chainerrl) - A deep reinforcement learning library built on top of Chainer.
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+## Distributed Computing
+* [Horovod](https://github.com/uber/horovod) - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [PySpark](https://spark.apache.org/docs/0.9.0/python-programming-guide.html) - Exposes the Spark programming model to Python. <img height="20" src="img/spark_big.png" alt="Apache Spark based">
+* [Veles](https://github.com/Samsung/veles) - Distributed machine learning platform.
+* [Jubatus](https://github.com/jubatus/jubatus) - Framework and Library for Distributed Online Machine Learning.
+* [DMTK](https://github.com/Microsoft/DMTK) - Microsoft Distributed Machine Learning Toolkit.
+* [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning.
+* [dask-ml](https://github.com/dask/dask-ml) - Distributed and parallel machine learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [Distributed](https://github.com/dask/distributed) - Distributed computation in Python.
 
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 ## Probabilistic Methods