Dear colleagues,
Our next BeNeRL Reinforcement Learning Seminar (Jan 11) is coming: Speaker: Chris Lu (https://chrislu.pagehttps://chrislu.page/), PhD student at the University of Oxford. Title: Accelerating RL research with PureJaxRL Date: January 11, 16.00-17.00 (CET) Please find full details about the talk below this email and on the website of the seminar series: https://www.benerl.org/seminar-series
The goal of the online BeNeRL seminar series is to invite RL researchers (mostly advanced PhD or early postgraduate) to share their work. In addition, we invite the speakers to briefly share their experience with large-scale deep RL experiments, and their style/approach to get these to work.
We would be very glad if you forward this invitation within your group and to other colleagues that would be interested (also outside the BeNeRL region). Hope to see you on January 11!
Kind regards, Zhao Yang & Thomas Moerland Leiden University
——————————————————————
Upcoming talk:
Date: January 11, 16.00-17.00 (CET) Speaker: Chris Lu (https://chrislu.pagehttps://chrislu.page/) Title: Accelerating RL research with PureJaxRL Zoom: https://universiteitleiden.zoom.us/j/65411016557?pwd=MzlqcVhzVzUyZlJKTEE0Nk5... Abstract: Recent advancements in JAX have enabled researchers to train RL agents entirely on the accelerator end-to-end, resulting in runtime speedups of over 4000x. Such speedups have the potential to fundamentally change the way we do RL, allowing researchers to efficiently run hundreds of seeds simultaneously, perform rapid hyperparameter tuning, and perform long-horizon meta-evolution for RL. Furthermore, this vastly lowers the computational barrier of entry to Deep RL research, allowing academic labs to perform research using trillions of frames (closing the gap with industry research labs) and enabling independent researchers to get orders of magnitude more mileage out of a single GPU. Bio: Chris Lu is a third-year DPhil student at the University of Oxford, where he is advised by Professor Jakob Foerster at FLAIR. His work focuses on applying evolution-inspired techniques to meta-learning and multi-agent reinforcement learning. In the summer of 2022 he interned at DeepMind as a research scientist. Previously, he worked as a researcher at Covariant.ai.