BeNeRL Reinforcement Learning Seminar: Ademi Adeniji from UC Berkeley – October 10
Join the upcoming BeNeRL Reinforcement Learning Seminar with Ademi Adeniji from UC Berkeley on October 10, discussing the use of foundation models for decision-making and novel pretraining approaches for reinforcement learning.
The next BeNeRL Reinforcement Learning Seminar will take place on October 10, featuring Ademi Adeniji, a PhD student from UC Berkeley. The title of his talk is Reinforcement Learning Behavioral Generalists – Top-Down and Bottom-Up. The seminar will be held online via Zoom, starting at 16.00-17.00 (CET). In this talk, Ademi will discuss how foundation models trained via conventional methods can enhance decision-making, and explore novel, scalable pretraining approaches that are native to control and hold promise for endowing artificial agents with stronger forms of behavioral generalization.
Ademi Adeniji is a Computer Science PhD student at UC Berkeley, advised by Pieter Abbeel. His research interests lie in creating agents capable of developing general-purpose intelligent behaviors through data and experience. He previously interned at NVIDIA where he worked on reinforcement learning and robotics. He completed his BS and MS at Stanford University conducting research in the Stanford Vision and Learning Lab advised by Fei-Fei Li. He is supported by the Berkeley Chancellors Fellowship.
Tags: BeNeRL, Reinforcement Learning Seminar, Ademi Adeniji, UC Berkeley, RL, Artificial Intelligence, Foundation Models, Decision-making, Scalable Pretraining