upd

Author: RiccZamboni

_pages/about.md

@@ -13,11 +13,9 @@ Briefly
 I am a PhD Student under the supervision of Prof. [Marcello Restelli](http://home.deib.polimi.it/restelli/MyWebSite/index.shtml), at the Department of Electronics, Informatics and Bio-engineering
 ([DEIB](https://www.deib.polimi.it/)) of Politecnico di Milano.
 
-You can find my (mostly) up to date CV [here](/files/Academic_CV.pdf){:target="_blank"}{:rel="noopener noreferrer"}. Turn to my Scholar or find me on BlueSky to catch up with more recent agenda.
+You can find my (mostly) up to date CV [here](/files/Academic_CV.pdf){:target="_blank"}{:rel="noopener noreferrer"}. My Scholar or BlueSky profile might be more up-to-date with my most recent agenda. Also, feel free to drop an email for any inquiries or questions!
 
-Feel free to drop an email for inquiries and questions!
-
-My research interest is in Reinforcement Learning (RL). Generally, I am investigating how to pass over common requirements, like data abundance (i.e. fast simulation) or centralized training in the presence of many agents; often rather limiting requirements indeed. More specifically, my current research tackles what can be done a priori of a task specification: what kind of pre-training can be done over decision-making policies to make RL easier. More broadly, my aim is to advance theoretical understanding that can lead to successful application of RL in the real world. These include the study of partial observability, RL with general utilities, multi-agent RL among others. I am passionate about applying RL to challenging and real-world tasks, currently I am collaborating with [Siemens AT](https://new.siemens.com/at/de.html) on applying scalable MARL techniques for Industry 4.0 and Industrial Production Scheduling.
+My research interest lies in Reinforcement Learning (RL). Generally, I am interested in how to overcome common limitations, such as the need for data abundance (i.e., fast simulation) or centralized training when many agents are involved; these are often rather limiting requirements indeed. More specifically, my current research explores what can be done prior to task specification: what kind of pre-training can enhance decision-making policies to simplify RL. More broadly, my aim is to advance theoretical understanding that can lead to successful application of RL in the real world. This calls for the study of partial observability, multi-agent scenarios, and reinforcement learning with general utilities, among others. I am passionate about applying RL to challenging and real-world tasks, and I am currently collaborating with [Siemens AT](https://new.siemens.com/at/de.html) on applying scalable MARL techniques for Industry 4.0 and Industrial Production Scheduling.
 
 
 News
@@ -26,12 +24,12 @@ News
 - Our work on [Scalable Multi-Agent Offline Reinforcement Learning
 and the Role of Information](https://arxiv.org/abs/2502.11260) was accepted at RLDM 2025!
 - I am visiting S. V. Albrecht and [David Abel](https://david-abel.github.io) at the [Autonomous Agents Research Group](https://agents-lab.org) in Edinburgh, U.K.
-- I am giving a talk to the [RL Virtual Reading Group](https://agents.inf.ed.ac.uk/reading-group/) about pushing forward our understanding of learning purely explorative policies in POMDPs, you can find the recording on [You Tube](https://www.youtube.com/watch?v=hAxd6--b7TM).
+- I am giving a talk to the [RL Virtual Reading Group](https://agents.inf.ed.ac.uk/reading-group/) about advancing our understanding of learning purely explorative policies in POMDPs. You can find the recording on [You Tube](https://www.youtube.com/watch?v=hAxd6--b7TM).
 - Grateful to be part of the Best Reviewer Award team at [ICML2024](https://icml.cc/virtual/2024/awards_detail).
 - I am attending the [Machine Learning Summer School](https://groups.oist.jp/mlss) in Onna, Japan.
 - Our paper [The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough](https://rlj.cs.umass.edu/2024/papers/RLJ_RLC_2024_95.pdf) was accepted at the just born [RLC](https://rl-conference.cc/2024/index.html) conference!
 - Our paper [How to explore with belief: state entropy maximization in POMDPs](https://dl.acm.org/doi/10.5555/3692070.3694469) was accepted at ICML 2024.
 - I am attending the [Reinforcement Learning Summer School](https://rlsummerschool.com/) in Barcelona, Spain.
 - I am taking part to the organization of the [15th European Workshop on Reinforcement Learning](https://ewrl.wordpress.com/past-ewrl/ewrl15-2022/) in Milan, Italy.
-- The first paper of my Phd [Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning](https://proceedings.neurips.cc/paper_files/paper/2023/hash/2a98af4fea6a24b73af7b588ca95f755-Abstract-Conference.html) was accepted at NeurIPs 2023!
+- The first paper of my Ph.D. [Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning](https://proceedings.neurips.cc/paper_files/paper/2023/hash/2a98af4fea6a24b73af7b588ca95f755-Abstract-Conference.html) was accepted at NeurIPs 2023!