Update README.md

Author: krzjoa

README.md

@@ -204,6 +204,7 @@
 
 ### Data Frames
 * [pandas](https://pandas.pydata.org/pandas-docs/stable/) - Powerful Python data analysis toolkit.
+* [polars](https://github.com/pola-rs/polars) - A fast multi-threaded, hybrid-out-of-core DataFrame library.
 * [pandas_profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects
 * [cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library. <img height="20" src="img/pandas_big.png" alt="pandas compatible"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 * [blaze](https://github.com/blaze/blaze) - NumPy and pandas interface to Big Data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
@@ -216,12 +217,9 @@
 * [koalas](https://github.com/databricks/koalas) - pandas API on Apache Spark. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
 * [modin](https://github.com/modin-project/modin) - Speed up your pandas workflows by changing a single line of code. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
 * [swifter](https://github.com/jmcarpenter2/swifter) - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
-* [pandas_flavor](https://github.com/Zsailer/pandas_flavor) - A package that allows writing your own flavor of Pandas easily.
 * [pandas-log](https://github.com/eyaltrabelsi/pandas-log) - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
 * [vaex](https://github.com/vaexio/vaex) - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
 * [xarray](https://github.com/pydata/xarray) - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less error-prone indexing routines.
-* [sk-transformer](https://github.com/chrislemke/sk-transformers) - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [polars](https://github.com/pola-rs/polars) - A fast multi-threaded, hybrid-out-of-core DataFrame library.
 
 
 ### Pipelines
@@ -258,6 +256,8 @@
 * [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [dirty_cat](https://github.com/dirty-cat/dirty_cat) - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression). <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [NitroFE](https://github.com/NITRO-AI/NitroFE) - Moving window features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [sk-transformer](https://github.com/chrislemke/sk-transformers) - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+
 
 ### Feature Selection
 * [scikit-feature](https://github.com/jundongl/scikit-feature) - Feature selection repository in Python.
@@ -332,38 +332,40 @@
 * [mxboard](https://github.com/awslabs/mxboard) - Logging MXNet data for visualization in TensorBoard. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
 
 ## Reinforcement Learning
-* [OpenAI Gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.
-* [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.
-* [Stable Baselines](https://github.com/hill-a/stable-baselines) - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
+* [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly [Gym](https://github.com/openai/gym)).
+* [Stable Baselines3](https://github.com/DLR-RM/stable-baselines3) - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
 * [RLlib](https://ray.readthedocs.io/en/latest/rllib.html) - Scalable Reinforcement Learning.
-* [Horizon](https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement Learning.
+* [Acme](https://github.com/google-deepmind/acme) - A library of reinforcement learning components and agents.
+* [Catalyst-RL](https://github.com/catalyst-team/catalyst-rl) - PyTorch framework for RL research.
+* [d3rlpy](https://github.com/takuseno/d3rlpy) - An offline deep reinforcement learning library.
 * [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.
 * [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.
+* [garage](https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.
+* [Horizon](https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement Learning.
+
+## Probabilistic Graphical Models
+* [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python. <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
+* [pgmpy](https://github.com/pgmpy/pgmpy) - A python library for working with Probabilistic Graphical Models.
+* [pyAgrum](https://agrum.gitlab.io/) - A GRaphical Universal Modeler.
 
 ## Probabilistic Methods
 * [pyro](https://github.com/uber/pyro) - A flexible, scalable deep probabilistic programming library built on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python. <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 * [ZhuSuan](http://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [PyMC](https://github.com/pymc-devs/pymc) - Bayesian Stochastic Modelling in Python.
 * [InferPy](https://github.com/PGM-Lab/InferPy) - Deep Probabilistic Modelling Made Easy. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [GPflow](http://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [PyStan](https://github.com/stan-dev/pystan) - Bayesian inference using the No-U-Turn sampler (Python interface).
 * [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes) - Python package for Bayesian Machine Learning with scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [pgmpy](https://github.com/pgmpy/pgmpy) - A python library for working with Probabilistic Graphical Models.
 * [skpro](https://github.com/alan-turing-institute/skpro) - Supervised domain-agnostic prediction framework for probabilistic modelling by [The Alan Turing Institute](https://www.turing.ac.uk/). <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [PtStat](https://github.com/stepelu/ptstat) - Probabilistic Programming and Statistical Inference in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [PyVarInf](https://github.com/ctallec/pyvarinf) - Bayesian Deep Learning methods with Variational Inference for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [emcee](https://github.com/dfm/emcee) - The Python ensemble sampling toolkit for affine-invariant MCMC.
 * [hsmmlearn](https://github.com/jvkersch/hsmmlearn) - A library for hidden semi-Markov models with explicit durations.
 * [pyhsmm](https://github.com/mattjj/pyhsmm) - Bayesian inference in HSMMs and HMMs.
 * [GPyTorch](https://github.com/cornellius-gp/gpytorch) - A highly efficient and modular implementation of Gaussian Processes in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [MXFusion](https://github.com/amzn/MXFusion) - Modular Probabilistic Programming on MXNet. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
 * [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite) - A scikit-learn-inspired API for CRFsuite. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 
 ## Genetic Programming