diff --git a/examples/DDPG/README.md b/examples/DDPG/README.md
index d7794617a2b93cc98f4ca760d6960ea10defb0cc..1fb54967a1dacadd8a56e27ffada6f2f128ce368 100644
--- a/examples/DDPG/README.md
+++ b/examples/DDPG/README.md
@@ -1,7 +1,7 @@
 ## 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
+> DDPG in
 [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)
 
 ### Mujoco games introduction
diff --git a/examples/DQN/README.md b/examples/DQN/README.md
index 2281cee4e5080a0030926a7f81ec5d4cdf7d82ec..14646f5034dc6826e737ce616e1a757d88ff7d91 100644
--- a/examples/DQN/README.md
+++ b/examples/DQN/README.md
@@ -1,7 +1,7 @@
 ## Reproduce DQN with PARL
 Based on PARL, we provide a simple demonstration of DQN.
 
-+ DQN in
+> DQN in
 [Human-level Control Through Deep Reinforcement Learning](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
 
 ### Result
diff --git a/examples/DQN_variant/README.md b/examples/DQN_variant/README.md
index 351e44754ad82125eec4e1346fd6301e8c1555b7..6de3a248fae17ff725767b6de9450a0266562f69 100644
--- a/examples/DQN_variant/README.md
+++ b/examples/DQN_variant/README.md
@@ -1,8 +1,11 @@
 ## Reproduce DQN with PARL
 Based on PARL, the DQN algorithm of deep reinforcement learning has been reproduced, reaching the same level of indicators as the paper in Atari benchmarks.
 
-+ DQN in
-[Human-level Control Through Deep Reinforcement Learning](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
++ 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
 Please see [here](https://gym.openai.com/envs/#atari) to know more about Atari games.
diff --git a/examples/IMPALA/README.md b/examples/IMPALA/README.md
index 6d96331aa049fc21eae4f288c47eaba6ab48cec6..cec4eaa89edfe3747cdef58e49cbca0ca6ac7721 100755
--- a/examples/IMPALA/README.md
+++ b/examples/IMPALA/README.md
@@ -1,7 +1,7 @@
 ## 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 in
 [Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures](https://arxiv.org/abs/1802.01561)
 
 ### Atari games introduction
diff --git a/examples/MADDPG/README.md b/examples/MADDPG/README.md
index 0bf3a599e76a3ecc127385baa0f4d81e47e3662b..b20d45976160f4665c2b109483db1f6b191aded7 100644
--- a/examples/MADDPG/README.md
+++ b/examples/MADDPG/README.md
@@ -1,7 +1,7 @@
 ## Reproduce MADDPG with PARL
 Based on PARL, the MADDPG algorithm of deep reinforcement learning has been reproduced.
 
-+ paper: 
+> MADDPG in 
 [ Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://arxiv.org/abs/1706.02275)
 
 ### Multi-agent particle environment introduction