Gautham Vasan

Congrats @RichardSSutton and Andy! You've built an incredible community and shaped the thinking of many researchers, including myself. Well deserved!

I'm in Vancouver for #NeurIPS2024. Ping me if you're around and want to chat about deep RL, robotics, continual learning and such!

If you are interested in working with me at *the* RL powerhouse @UAlberta on robot learning on physical robots, please drop me a message. Retweets welcome 🙏

I'm giving a talk next week on our recent NeurIPS paper, "Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers". Feel free to join if you are available! :) Thank you @CohereForAI for hosting me! 📜 arxiv.org/abs/2411.15370

Would you believe that deep RL can work without replay buffers, target networks, or batch updates? Our recent work gets deep RL agents to learn from a continuous stream of data one sample at a time without storing any sample. Joint work with @Gautham529 and @rupammahmood.

I'm presenting our work on reward design and robot learning at RLC today! We show that it is possible to learn pixel-based policies from scratch on real robots within two to three hours using only sparse rewards 🤖 📜 arxiv.org/abs/2407.00324 🎞️ youtu.be/a6zlVUuKzBc?si… #RLC

Check out our research on how to specify rewards in RL: TL;DR: Sparse reward formulations can lead to higher-quality policies and outperform dense reward policies on their own metrics. Paper 📜: arxiv.org/abs/2407.00324 #reinforcementlearning #robotics

#AGIComics offers a definition of AGI that is achievable, based on economics, and provably safe.

Come checkout our cool poster @AAMASconf this week! 🇳🇿

Trying to put myself out there more often. Some thoughts on Moravec's paradox, sim-to-real transfer, and robot learning: gauthamvasan.github.io/posts/simulato… Hope you find it interesting! #AI #Robotics

Hope to see you at @AAMASconf 2024 in New Zealand! Thank you to my co-authors Tristan Tomilin, @Gautham529, Matt Taylor, @rupammahmood, Meng Fang, @pechenizkiy, and Decebal Mocanu. 📜arxiv.org/abs/2312.15339 💻github.com/bramgrooten/ma…

Explored some practical, oft-ignored challenges in continual learning on real-world robots in a recent AI Seminar, delving into: 1️⃣ Specifying reinforcement learning tasks 2️⃣ Setting up a real-time learning agent youtu.be/QO0mmHMJvRQ?si… 🤖

Thank you @neuralbertatech, for organizing the natChat 2023 event and making my invited talk public! youtu.be/kkJ8-k-_CPQ #reinforcementlearning #robotics

A packed house to hear @BFlanaganUofA from the @UAlberta and @AmiiThinks announce that 20 new faculty will be hired in AI across campus in the next 3 years, with 5 of these positions in CS.

Some of my recent work with @rupammahmood on real-time vision-based learning from scratch on robots! #Robotics #RL

We are excited to announce the launch of SenseAct, the first reinforcement learning open-source toolkit for physical robots businesswire.com/news/home/2018… #reinforcementlearning #Robotics #machinelearning #robots