Feat: Update Readme

Author: choru-k

README.md

@@ -15,7 +15,7 @@ So you can run this example in your computer(maybe it take just only 1~2 minitue
 - [x] Distributional(C51) [[7]](#reference)
 - [x] Rainbow [[8]](#reference)
 
-## PG
+## PG(Policy Gradient)
 - [x] REINFORCE [[9]](#reference)
 - [x] Actor Critic [[10]](#reference)
 - [x] Advantage Actor Critic
@@ -33,12 +33,14 @@ So you can run this example in your computer(maybe it take just only 1~2 minitue
 - [ ] IMPALA [[23]](#reference)
 - [ ] R2D2 [[16]](#reference)
 
-## Will
-- [ ] RND [[17]](#reference)
-- [ ] ICM [[22]](#refercence)
+## Distributional DQN
 - [ ] QRDQN [[18]](#reference)
 - [ ] IQN [[19]](#reference)
 
+## Exploration
+- [ ] RND [[17]](#reference)
+- [ ] ICM [[22]](#refercence)
+
 
 ## Reference
 [1][Playing Atari with Deep Reinforcement Learning](http://arxiv.org/abs/1312.5602)  
@@ -62,7 +64,7 @@ So you can run this example in your computer(maybe it take just only 1~2 minitue
 [19][Implicit Quantile Networks for Distributional Reinforcement Learning](https://arxiv.org/pdf/1806.06923.pdf)  
 [20][A Natural Policy Gradient](https://papers.nips.cc/paper/2073-a-natural-policy-gradient.pdf)  
 [21][SAMPLE EFFICIENT ACTOR-CRITIC WITH EXPERIENCE REPLAY](https://arxiv.org/pdf/1611.01224.pdf)  
-[22][Curiosity-driven Exploration by Self-supervised Prediction](https://arxiv.org/pdf/1705.05363.pdf)
+[22][Curiosity-driven Exploration by Self-supervised Prediction](https://arxiv.org/pdf/1705.05363.pdf)  
 [23][IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://arxiv.org/pdf/1802.01561.pdf)