Deep RL Course
@DeepRLCourse
Deep Reinforcement Learning Sharif University of Technology
What makes RL hard is the _time_ axis⏳, so let's pre-train RL policies to learn about _time_! Same intuition as successor representations 🧠, but made scalable with modern GenAI models 🚀. Excited to share new work led by @chongyiz1, together with @seohong_park and @svlevine!
1/ How should RL agents prepare to solve new tasks? While prior methods often learn a model that predicts the immediate next observation, we build a model that predicts many steps into the future, conditioning on different user intentions: chongyi-zheng.github.io/infom.
Agreed 💯
Ilya Sutskever, in his speech at UToronto 2 days ago: "The day will come when AI will do all the things we can do." "The reason is the brain is a biological computer, so why can't the digital computer do the same things?" It's funny that we are debating if AI can "truly think"…
AI-GAs FTW (eg DGM, ADAS, OMNI, etc.) 🚀🔥🧠📈
Richard Sutton says that the current dominance of LLMs is a "momentary fixation" the real breakthroughs will come from scaling computation, not building AI systems based on how humans think they work. LLMs will not be the leading edge of AI for more than another decade, perhaps…
ACM has made an excellent video introduction to reinforcement learning!
2024 @TheOfficialACM A.M. Turing Award recipients @RichardSSutton and Andrew G. Barto discuss their #careers and their work on reinforcement learning in an #original #video at bit.ly/43fpe4q