Learning with Opponent-Learning Awareness
Presented by Jakob Foerster from University of Oxford, this webinar discusses deep reinforcement learning in multi-agent settings and examines how learning occurs in situations where multiple agents are learning at the same time in a decentralized fashion. It is focused on “Learning with Opponent-Learning Awareness” (LOLA), a novel multi-agent reinforcement learning method that allows one agent to consider the learning dynamics of another agent. We show that this not only stabilizes learning in multi-agent settings, but also leads to emergence of cooperation.
By watching this webinar replay, you'll learn:
- how to apply reinforcement learning to multi-agent systems
- the challenges of reciprocity in multi-agent reinforcement learning; and
- why “Learning with Opponent Learning Awareness” (LOLA) can help AI systems learn to navigate areas of conflicting interests
ON DEMAND WEBINAR REGISTRATION
Presented By
Jakob Foerster
PhD student in Machine Learning, University of Oxford
Jakob Foerster is a Ph.D. student in AI at the University of Oxford. Using recurrent deep reinforcement learning, he studies a range of multi-agent problems, ranging from the emergence of communication to addressing non-stationarity, credit-assignment and reciprocity in Deep Multi-agent RL. Jakob also interned at Google Brain, OpenAI, and DeepMind. Previously, Jakob spent four years working at Google and Goldman Sachs. He has also worked on a number of research projects in systems neuroscience, including work at the Massachusetts Institute of Technology and research at the Weizmann Institute of Science.
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