readme SAC vs. SAC-Discrete separated out

Author: p-christ

README.md

@@ -19,7 +19,8 @@ This repository contains PyTorch implementations of deep reinforcement learning
 1. *REINFORCE* <sub><sup> ([Williams et al. 1992](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)) </sup></sub>
 1. *Deep Deterministic Policy Gradients (DDPG)* <sub><sup> ([Lillicrap et al. 2016](https://arxiv.org/pdf/1509.02971.pdf) ) </sup></sub>
 1. *Twin Delayed Deep Deterministic Policy Gradients (TD3)* <sub><sup> ([Fujimoto et al. 2018](https://arxiv.org/abs/1802.09477)) </sup></sub>
-1. *Soft Actor-Critic (SAC & SAC-Discrete)* <sub><sup> ([Haarnoja et al. 2018](https://arxiv.org/pdf/1812.05905.pdf) and [Christodoulou 2019](https://arxiv.org/abs/1910.07207)) </sup></sub> 
+1. *Soft Actor-Critic (SAC)* <sub><sup> ([Haarnoja et al. 2018](https://arxiv.org/pdf/1812.05905.pdf)) </sup></sub>
+1. *Soft Actor-Critic for Discrete Actions (SAC-Discrete)* <sub><sup> ([Christodoulou 2019](https://arxiv.org/abs/1910.07207)) </sup></sub> 
 1. *Asynchronous Advantage Actor Critic (A3C)* <sub><sup> ([Mnih et al. 2016](https://arxiv.org/pdf/1602.01783.pdf)) </sup></sub>
 1. *Syncrhonous Advantage Actor Critic (A2C)*
 1. *Proximal Policy Optimisation (PPO)* <sub><sup> ([Schulman et al. 2017](https://openai-public.s3-us-west-2.amazonaws.com/blog/2017-07/ppo/ppo-arxiv.pdf)) </sup></sub>