Update README.md

Author: quantumiracle

README.md

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-# Popular Model-free Reinforcement Learning Algorithms  [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=State-of-the-art-Model-free-Reinforcement-Learning-Algorithms%20&url=hhttps://github.com/quantumiracle/STOA-RL-Algorithms&hashtags=RL)
+# Popular Model-free Reinforcement Learning Algorithms  
+<!-- [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=State-of-the-art-Model-free-Reinforcement-Learning-Algorithms%20&url=hhttps://github.com/quantumiracle/STOA-RL-Algorithms&hashtags=RL) -->
 
 
 **PyTorch** and **Tensorflow 2.0** implementation of state-of-the-art model-free reinforcement learning algorithms on both Openai gym environments and a self-implemented Reacher environment. 
 
-Algorithms include **Soft Actor-Critic (SAC), Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt (including Cross-entropy (CE) Method)**, **PointNet**, **Transporter**, **Recurrent Policy Gradient**, **Soft Decision Tree**, **Probabilistic Mixture-of-Experts**, etc.
+Algorithms include:
+* **Actor-Critic (AC/A2C)**;
+* **Soft Actor-Critic (SAC)**;
+* **Deep Deterministic Policy Gradient (DDPG)**;
+* **Twin Delayed DDPG (TD3)**; 
+* **Proximal Policy Optimization (PPO)**;
+* **QT-Opt (including Cross-entropy (CE) Method)**;
+* **PointNet**;
+* **Transporter**;
+* **Recurrent Policy Gradient**;
+* **Soft Decision Tree**;
+* **Probabilistic Mixture-of-Experts**;
+* **QMIX**
+* etc.
 
 Please note that this repo is more of a personal collection of algorithms I implemented and tested during my research and study period, rather than an official open-source library/package for usage. However, I think it could be helpful to share it with others and I'm expecting useful discussions on my implementations. But I didn't spend much time on cleaning or structuring the code. As you may notice that there may be several versions of implementation for each algorithm, I intentionally show all of them here for you to refer and compare. Also, this repo contains only **PyTorch** Implementation.