style(nyz): add DRL algo list and link

README.md

@@ -5,6 +5,7 @@ colorFrom: red
 colorTo: gray
 sdk: static
 pinned: false
+license: apache-2.0
 ---
 
 
@@ -22,27 +23,39 @@ OpenDILab contributes to the integration of the latest and most comprehensive ac
 If you want to contact us & join us, you can  ✉️  to our team : <opendilab@pjlab.org.cn>.
 
 
+
 # Overview of Model Zoo
 
-## Deep Reinforcement Learning
+<sup>(1): "-" means that this algorithm doesn't support this environment.</sup>
+<sup>(2): "W" means that the corresponding model is in the upload waitinglist.</sup>
+
+### Deep Reinforcement Learning
 
 | Algo.\Env.   | LunarLander | BipedalWalker | Pendulum | Atari (Pong) | Atari (SpaceInvaders) | Atari (Qbert) | MuJoCo (Hopper) | MuJoCo (Halfcheetah) | MuJoCo (Walker2d) |
 | ------------- | ------------- | ------------------------ | ------------ | -------------- | ------------ | ------------------ | --------- | --------- | --------- |
-| [PPO](https://arxiv.org/abs/1707.06347) | [Model](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-ppo) | | | | | | | | |
+| [PPO](https://arxiv.org/pdf/1707.06347.pdf) | [√](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-ppo) | | | | | | | | |
+| [PG](https://proceedings.neurips.cc/paper/1999/file/464d828b85b0bed98e80ade0a5c43b0f-Paper.pdf) | | | | | | | | | |
+| [A2C](https://arxiv.org/pdf/1602.01783.pdf) | | | | | | | | | |
+| [IMPALA](https://arxiv.org/pdf/1802.01561.pdf) | | | | | | | | | |
+| [DQN](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) | | | | | | | - | - | - |
+| [DDPG](https://arxiv.org/pdf/1509.02971.pdf) | | | | - | - | - | | | |
+| [TD3](https://arxiv.org/pdf/1802.09477.pdf) | | | | - | - | - | | | |
+| [SAC](https://arxiv.org/pdf/1801.01290.pdf) | | | | - | - | - | | | |
+
 
-## Multi-Agent Reinforcement Learning
+### Multi-Agent Reinforcement Learning
 <details close>
 <summary>(Click for Details)</summary>
 TBD
 </details>
 
-## Offline Reinforcement Learning
+### Offline Reinforcement Learning
 <details close>
 <summary>(Click for Details)</summary>
 TBD
 </details>
 
-## Model-Based Reinforcement Learning
+### Model-Based Reinforcement Learning
 <details close>
 <summary>(Click for Details)</summary>
 TBD