wz

index.html

@@ -31,7 +31,7 @@ <h1>Zhi Wang</h1>
 Department of Control Science and Intelligent Engineering<br /></p>
 <p>22 Hankou Road, Gulou District, Nanjing, China <br /> 
 E-mail:  <a href="mailto:[email protected]">[email protected]</a></p>
-<p>Reinforcement Learning, Robot Learning, Meta / Offline / Multi-Agent RL </p>
+<p>Reinforcement Learning, Robot Learning, In-context / Offline / Multi-Agent RL </p>
 <p><a href="https://scholar.google.com/citations?user=cRXlxYcAAAAJ&hl=en"><img src="scholar.png" alt="alt text" width="30px" height="30px" /></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
 <a href="https://github.com/HeyuanMingong"><img src="github.png" alt="alt text" width="30px" height="30px" /></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
 <a href="https://sme.nju.edu.cn/wz1/list.htm"><img src="nju.png" alt="alt text" width="30px" height="30px" /></a> </p>
@@ -49,7 +49,7 @@ <h2>About me</h2>
 <br />
 Specifically, I work on how learning algorithms can scale RL agents to (i) dynamic environments, (ii) offline settings, and (iii) multi-agent systems, allowing them to autonomously adapt to (i) non-stationary task distributions, (ii) non-interactive scenarios, and (iii) cooperative or competitive task assignments in real-world domains. 
 <br />
-This includes a wide range of topics such as (i) meta-RL, lifelong/continual RL, in-context RL, (ii) offline RL, large models for RL, and (iii) multi-agent RL.
+This includes a wide range of topics such as (i) in-context RL, meta-RL, lifelong/continual RL, (ii) offline RL, large models for RL, and (iii) multi-agent RL.
 </p>
 
 <p>

publication.html

@@ -35,7 +35,7 @@ <h3>Preprints</h3>
 <h3>Conferences</h3>
 <ol>
 
- <li><p>Zhi Wang, Li Zhang, Wenhao Wu, Yuanheng Zhu, Dongbin Zhao, and Chunlin Chen, "Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement," in <i>Advances of Neural Information Processing Systems (NeurIPS)</i>, 2024.
+ <li><p><b>Zhi Wang</b>, Li Zhang, Wenhao Wu, Yuanheng Zhu, Dongbin Zhao, and Chunlin Chen, "Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement," in <i>Advances of Neural Information Processing Systems (NeurIPS)</i>, 2024. [<a href="https://github.com/NJU-RL/Meta-DT">code</a>]
 </p></li> 
 
 <li><p>Zican Hu, Zongzhang Zhang, Huaxiong Li, Chunlin Chen, Hongyu Ding, and <b>Zhi Wang*</b>, "<a href="https://arxiv.org/abs/2312.04819">Attention-Guided Contrastive Role Representations for Multi-Agent Reinforcement Learning</a>," in <i>Proceedings of International Conference on Learning Representations (ICLR)</i>, 2024. [<a href="https://github.com/NJU-RL/ACORM">code</a>]