clean README

README.md

@@ -102,27 +102,7 @@ Traffic vehicles can not response to surrounding vchicles if directly replaying
 Add argument ```--reactive_traffic``` to use an IDM policy control them and make them reactive.
 Press key ```r``` for loading a new scenario, and ```b``` or ```q``` for switching perspective. 
 
-[comment]: <> (### LQY: avoid introducing these trivial things )
 
-[comment]: <> (Run the example of procedural generation of a new map as:)
-
-[comment]: <> (```bash)
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-[comment]: <> (python -m metadrive.examples.procedural_generation)
-
-[comment]: <> (```)
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-[comment]: <> (*Note that the scripts above can not be run in a headless machine.*)
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-[comment]: <> (*Please refer to the installation guideline in documentation for more information about how to launch runing in a headless machine.*)
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-[comment]: <> (Run the following command to draw the generated maps from procedural generation:)
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-[comment]: <> (```bash)
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-[comment]: <> (python -m metadrive.examples.draw_maps)
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-[comment]: <> (```)
 
 ### Basic Usage
 To build the RL environment in python script, you can simply code in the Farama Gymnasium format as:
@@ -167,66 +147,6 @@ If you use MetaDrive in your own work, please cite:
 }
 ```
 
-## 🎉 Relevant Projects
-
-**Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization**
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-Zhenghao Peng, Quanyi Li, Chunxiao Liu, Bolei Zhou 
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-*NeurIPS 2021*
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-[<a href="https://arxiv.org/pdf/2110.13827.pdf" target="_blank">Paper</a>]
-[<a href="https://github.com/decisionforce/CoPO" target="_blank">Code</a>]
-[<a href="https://decisionforce.github.io/CoPO" target="_blank">Webpage</a>]
-[<a href="https://decisionforce.github.io/CoPO/copo_poster.pdf" target="_blank">Poster</a>]
-[<a href="https://youtu.be/sOw43l8lwxE" target="_blank">Talk</a>]
-[<a href="https://github.com/metadriverse/metadrive-benchmark/tree/main/MARL" target="_blank">Results&Models</a>]
-
-
-**Safe Driving via Expert Guided Policy Optimization**
-\
-Zhenghao Peng*, Quanyi Li*, Chunxiao Liu, Bolei Zhou
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-*Conference on Robot Learning (CoRL) 2021*
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-[<a href="https://arxiv.org/pdf/2110.06831.pdf" target="_blank">Paper</a>]
-[<a href="https://github.com/decisionforce/EGPO" target="_blank">Code</a>]
-[<a href="https://decisionforce.github.io/EGPO/" target="_blank">Webpage</a>]
-[<a href="https://decisionforce.github.io/EGPO/images/egpo_poster.png" target="_blank">Poster</a>]
-
-**Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization**
-\
-Quanyi Li*, Zhenghao Peng*, Bolei Zhou
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-*ICLR 2022*
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-[<a href="https://arxiv.org/pdf/2202.10341.pdf" target="_blank">Paper</a>]
-[<a href="https://github.com/decisionforce/HACO" target="_blank">Code</a>]
-[<a href="https://decisionforce.github.io/HACO/" target="_blank">Webpage</a>]
-[<a href="https://github.com/decisionforce/HACO/blob/main/docs/iclr_poster.pdf" target="_blank">Poster</a>]
-[<a href="https://youtu.be/PiJv4wtp8T8" target="_blank">Talk</a>]
-
-**Human-AI Shared Control via Policy Dissection**
-\
-Quanyi Li, Zhenghao Peng, Haibin Wu, Lan Feng, Bolei Zhou
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-*NeurIPS 2022*
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-[<a href="https://arxiv.org/pdf/2206.00152.pdf" target="_blank">Paper</a>]
-[<a href="https://github.com/metadriverse/policydissect" target="_blank">Code</a>]
-[<a href="https://metadriverse.github.io/policydissect/" target="_blank">Webpage</a>]
-
-
-And more:
-
-
-* Yang, Yujie, Yuxuan Jiang, Yichen Liu, Jianyu Chen, and Shengbo Eben Li. "Model-Free Safe Reinforcement Learning through Neural Barrier Certificate." IEEE Robotics and Automation Letters (2023).
-
-* Feng, Lan, Quanyi Li, Zhenghao Peng, Shuhan Tan, and Bolei Zhou. "TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios." (**ICRA 2023**)
-
-* Zhenghai Xue, Zhenghao Peng, Quanyi Li, Zhihan Liu, Bolei Zhou. "Guarded Policy Optimization with Imperfect Online Demonstrations." (**ICLR 2023**)
-
-
 
 ## Acknowledgement