Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+<div align="center">
+  <div style="margin-bottom: 30px">  
+    <div style="display: flex; flex-direction: column; align-items: center; gap: 8px"> 
+      <h1 align="center" style="margin: 0; line-height: 1;">
+        <span style="font-size: 48px; font-weight: 600;">PSEC</span>
+      </h1>
+    </div>
+    <h2 style="font-size: 32px; margin: 20px 0;">Skill Expansion and Composition in Parameter Space</h2>
+    <h4 style="color: #666; margin-bottom: 25px;">International Conference on Learning Representation (ICLR), 2025</h4>
+    <p align="center" style="margin: 20px 0;">
+      <a href="https://arxiv.org/abs/2502.05932">
+        <img src="https://img.shields.io/badge/arXiv-2502.05932-b31b1b.svg">
+      </a>
+      <!-- &nbsp;&nbsp; -->
+      <a href="https://ltlhuuu.github.io/PSEC/">
+        <img src="https://img.shields.io/badge/🌐_Project_Page-PSEC-blue.svg">
+      </a>
+      <!-- &nbsp;&nbsp; -->
+      <a href="https://arxiv.org/pdf/2502.05932.pdf">
+        <img src="https://img.shields.io/badge/📑_Paper-PSEC-green.svg">
+      </a>
+    </p>
+  </div>
+</div>
+
+<div align="center">
+  <p style="font-size: 20px; font-weight: 600; margin-bottom: 20px;">
+    🔥 Official Implementation
+  </p>
+  <p style="font-size: 18px; max-width: 800px; margin: 0 auto;">
+          <b>PSEC</b> is a novel framework designed to:
+  </p>
+</div>
+<div align="center">
+  <p style="font-size: 15px; font-weight: 600; margin-bottom: 20px;">
+    🚀 <b>Facilitate</b> efficient and flexible skill expansion and composition <br>
+     🔄 <b>Iteratively evolve</b> the agents' capabilities<br>
+      ⚡ <b>Efficiently address</b> new challenges
+  </p>
+</div>
+
+<p align="center">
+ <img src="assets/intro.png" width="800" style="margin: 40px 0;">
+</p>
+<!-- <div align="center">
+ <a href="https://github.com/ltlhuuu/PSEC/stargazers">
+   <img src="https://img.shields.io/github/stars/ltlhuuu/PSEC?style=social" alt="GitHub stars">
+ </a>
+ &nbsp;
+ <a href="https://github.com/ltlhuuu/PSEC/network/members">
+   <img src="https://img.shields.io/github/forks/ltlhuuu/PSEC?style=social" alt="GitHub forks">
+ </a>
+ &nbsp;
+ <a href="https://github.com/ltlhuuu/PSEC/issues">
+   <img src="https://img.shields.io/github/issues/ltlhuuu/PSEC?style=social" alt="GitHub issues">
+ </a>
+</div> -->
+
+
+## Quick start
+Clone this repository and navigate to PSEC folder
+```python
+git clone https://github.com/ltlhuuu/PSEC.git
+cd PSEC
+```
+## Environment Installation
+Environment configuration and dependencies are available in environment.yaml and requirements.txt.
+
+Create conda environment for this experiments
+```python
+conda create -n PSEC python=3.9
+conda activate PSEC
+```
+Then install the remaining requirements (with MuJoCo already downloaded, if not see [here](#MuJoCo-installation)): 
+```bash
+pip install -r requirements.txt
+```
+
+Install the `MetaDrive` environment via
+```python
+pip install git+https://github.com/HenryLHH/metadrive_clean.git@main
+```
+### MuJoCo installation
+Download MuJoCo:
+```bash
+mkdir ~/.mujoco
+cd ~/.mujoco
+wget https://github.com/google-deepmind/mujoco/releases/download/2.1.0/mujoco210-linux-x86_64.tar.gz
+tar -zxvf mujoco210-linux-x86_64.tar.gz
+cd mujoco210
+wget https://www.roboti.us/file/mjkey.txt
+```
+Then add the following line to `.bashrc`:
+```
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mujoco210/bin
+```
+## Run experiments
+### Pretrain
+Pretrain the model with the following command. Meanwhile there are pre-trained models, you can download them from [here](https://drive.google.com/drive/folders/1lpcShmYoKVt4YMH66JBiA0MhYEV9aEYy?usp=sharing).
+```python
+export XLA_PYTHON_CLIENT_PREALLOCATE=False
+CUDA_VISIBLE_DEVICES=0 python launcher/examples/train_pretrain.py --variant 0 --seed 0
+```
+### LoRA finetune
+Train the skill policies with LoRA to achieve skill expansion. Meanwhile there are pre-trained models, you can download them from [here](https://drive.google.com/drive/folders/1lpcShmYoKVt4YMH66JBiA0MhYEV9aEYy?usp=sharing).
+```python
+CUDA_VISIBLE_DEVICES=0 python launcher/examples/train_lora_finetune.py --com_method 0 --model_cls 'LoRALearner' --variant 0 --seed 0
+```
+### Context-aware Composition
+Train the context-aware modular to adaptively leverage different skill knowledge to solve the tasks. You can download the pretrained model and datasets from [here](https://drive.google.com/drive/folders/1lpcShmYoKVt4YMH66JBiA0MhYEV9aEYy?usp=sharing). Then, run the following command,
+```python
+CUDA_VISIBLE_DEVICES=0 python launcher/examples/train_lora_finetune.py --com_method 0 --model_cls 'LoRASLearner' --variant 0 --seed 0
+```
+
+## Citations
+If you find our paper and code useful for your research, please cite:
+```
+@inproceedings{
+liu2025psec,
+title={Skill Expansion and Composition in Parameter Space},
+author={Tenglong Liu, Jianxiong Li, Yinan Zheng, Haoyi Niu, Yixing Lan, Xin Xu, Xianyuan Zhan},
+booktitle={The Thirteenth International Conference on Learning Representations},
+year={2025},
+url={https://openreview.net/forum?id=GLWf2fq0bX}
+}
+```
+
+## Acknowledgements
+
+Parts of this code are adapted from [IDQL](https://github.com/philippe-eecs/IDQL).