Update README.md

README.md

@@ -15,6 +15,8 @@
 RLzoo is a collection of the most practical reinforcement learning algorithms, frameworks and applications. It is implemented with Tensorflow 2.0 and API of neural network layers in TensorLayer 2, to provide a hands-on fast-developing approach for reinforcement learning practices and benchmarks. It supports basic toy-tests like [OpenAI Gym](https://gym.openai.com/) and [DeepMind Control Suite](https://github.com/deepmind/dm_control) with very simple configurations. Moreover, RLzoo supports robot learning benchmark environment [RLBench](https://github.com/stepjam/RLBench) based on  [Vrep](http://www.coppeliarobotics.com/)/[Pyrep](https://github.com/stepjam/PyRep) simulator. Other large-scale distributed training framework for more realistic scenarios with [Unity 3D](https://github.com/Unity-Technologies/ml-agents), 
 [Mujoco](http://www.mujoco.org/), [Bullet Physics](https://github.com/bulletphysics/bullet3), etc, will be supported in the future. A [Springer textbook](https://deepreinforcementlearningbook.org) is also provided, you can get the free PDF if your institute has Springer license.
 
+Different from RLzoo for simple usage with **high-level APIs**, we also have a RL tutorial that aims to make the reinforcement learning tutorial simple, transparent and straight-forward with **low-level APIs**, as this would not only benefits new learners of reinforcement learning, but also provide convenience for senior researchers to testify their new ideas quickly.
+
 <!-- <em>Gym: Atari</em>    <em>Gym: Box2D </em>   <em>Gym: Classic Control </em>  <em>Gym: MuJoCo </em>-->
 
 <img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/atari.gif" height=250 width=210 > <img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/box2d.gif" height=250 width=210 > <img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/classic.gif" height=250 width=210 > <img src="https://github.com/tensorlayer/RLzoo/blob/master/gif/mujoco.gif" height=250 width=210 >