Machine Learning Meetup
This weekly seminar series, hosted by the computer vision research team at FeatureX, is open to students and professionals who share an interest in gathering with like-minded machine learning researchers. This series focuses on current and influential papers in machine learning, and brings active participants together around one relevant paper each week. The presenter will introduce the background of the paper and review the findings. Attendees are expected to have read the paper and be ready to participate in group discussions about the research content and its implications.
Space is limited and RSVP’s are mandatory for this event. Please email Emily Rogers at firstname.lastname@example.org if you plan to attend. If your plans change, please update us so we can offer space to someone else.
Paper Title and Link: Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments by Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel
Abstract: Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of continuous adaptation into the learning-to-learn framework. We develop a simple gradient-based meta-learning algorithm suitable for adaptation in dynamically changing and adversarial scenarios. Additionally, we design a new multi-agent competitive environment, RoboSumo, and define iterated adaptation games for testing various aspects of continuous adaptation. We demonstrate that meta-learning enables significantly more efficient adaptation than reactive baselines in the few-shot regime. Our experiments with a population of agents that learn and compete suggest that meta-learners are the fittest.