| Website | https://www.conglu.co.uk/ |
| https://twitter.com/cong_ml |
RL agents 🤖 need a lot of data, which they usually need to gather themselves. But does that data need to be real? Enter *Synthetic Experience Replay*, leveraging recent advances in #GenerativeAI in order to vastly upsample ⬆️ an agent’s training data!
Paper: arxiv.org/abs/2303.06614
💥 ML Research Opportunity for all under-represented undergrads at the University of Oxford! 💥
Would appreciate help sharing this widely! UNIQ+ is an awesome way to spend two months getting stuck into ML in great research groups.
See proposed projects here: https://www.ox.ac.uk/admissions/graduate/access/uniq-plus/projects
📣 You can now find *V-D4RL*, a benchmarking suite for offline RL from pixels, on #huggingface:
https://huggingface.co/datasets/conglu/vd4rl 🚀
Highlights:
💥 New D4RL-style visual datasets!
💥 Competitive baselines based on Dreamer and DrQ!
💥 A set of exciting open problems!
This is joint work with @conglu, Phil Ball, @jparkerholder, @maosbot, and @yeewhye !
Now time for a first research post...
No better time to start on offline RL from pixels! V-D4RL is now on #huggingface at https://huggingface.co/datasets/conglu/vd4rl
💥 New D4RL-style visual datasets!
💥 Competitive baselines based on Dreamer and DrQ!
💥 A set of exciting open problems!
Time for a very late introduction, I'm a 4th year PhD student at the University of Oxford interested in deep reinforcement learning, generative modelling, and Bayesian methods!
Most lately, been thinking about effective ways to automate reinforcement learning (PBT, HPO) and how to extend use cases for offline reinforcement learning (learning from pixels, generalizing to unseen environments)!
Always v. v. happy to chat :)
Tim G. J. Rudner