update readme (#346)

examples/DDPG/README.md

 ## Reproduce DDPG with PARL
 Based on PARL, the DDPG algorithm of deep reinforcement learning has been reproduced, reaching the same level of indicators as the paper in Atari benchmarks.
 
-> DDPG in
-[Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)
+> Paper: DDPG in [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)
 
 ### Mujoco games introduction
 Please see [here](https://github.com/openai/mujoco-py) to know more about Mujoco games.

examples/DQN/README.md

 ## Reproduce DQN with PARL
 Based on PARL, we provide a simple demonstration of DQN.
 
-> DQN in
-[Human-level Control Through Deep Reinforcement Learning](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
++ Paper: DQN in [Human-level Control Through Deep Reinforcement Learning](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
 
 ### Result
 

examples/DQN_variant/README.md

@@ -4,7 +4,9 @@ Based on PARL, the DQN algorithm of deep reinforcement learning has been reprodu
 + Papers: 
 
 > DQN in [Human-level Control Through Deep Reinforcement Learning](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
+
 > DDQN in [Deep Reinforcement Learning with Double Q-learning](https://arxiv.org/abs/1509.06461)
+
 > Dueling DQN in [Dueling Network Architectures for Deep Reinforcement Learning](https://arxiv.org/abs/1511.06581)
 
 ### Atari games introduction

examples/IMPALA/README.md

 ## Reproduce IMPALA with PARL
 Based on PARL, the IMPALA algorithm of deep reinforcement learning is reproduced, and the same level of indicators of the paper is reproduced in the classic Atari game.
 
-> IMPALA in
-[Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures](https://arxiv.org/abs/1802.01561)
+> Paper: IMPALA in [Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures](https://arxiv.org/abs/1802.01561)
 
 ### Atari games introduction
 Please see [here](https://gym.openai.com/envs/#atari) to know more about Atari games.

examples/MADDPG/README.md

 ## Reproduce MADDPG with PARL
 Based on PARL, the MADDPG algorithm of deep reinforcement learning has been reproduced.
 
-> MADDPG in 
-[ Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://arxiv.org/abs/1706.02275)
+> Paper: MADDPG in [ Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://arxiv.org/abs/1706.02275)
 
 ### Multi-agent particle environment introduction
 A simple multi-agent particle world based on gym. Please see [here](https://github.com/openai/multiagent-particle-envs) to install and know more about the environment.

examples/PPO/README.md

@@ -5,8 +5,7 @@ Include following approach:
 + Clipped Surrogate Objective
 + Adaptive KL Penalty Coefficient
 
-> PPO in
-[Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347)
+> Paper: PPO in [Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347)
 
 ### Mujoco games introduction
 Please see [here](https://github.com/openai/mujoco-py) to know more about Mujoco games.

examples/SAC/README.md

@@ -5,8 +5,7 @@ Include following approaches:
 + DDPG Style with Stochastic Policy
 + Maximum Entropy
 
-> SAC in
-[Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://arxiv.org/abs/1801.01290)
+> Paper: SAC in [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://arxiv.org/abs/1801.01290)
 
 ### Mujoco games introduction
 Please see [here](https://github.com/openai/mujoco-py) to know more about Mujoco games.

examples/TD3/README.md

@@ -6,8 +6,7 @@ Include following approaches:
 + Target Networks and Delayed Policy Update
 + Target Policy Smoothing Regularization
 
-> TD3 in
-[Addressing Function Approximation Error in Actor-Critic Methods](https://arxiv.org/abs/1802.09477)
+> Paper: TD3 in [Addressing Function Approximation Error in Actor-Critic Methods](https://arxiv.org/abs/1802.09477)
 
 ### Mujoco games introduction
 Please see [here](https://github.com/openai/mujoco-py) to know more about Mujoco games.