Deep Reinforcement Learning for Multi-Agent Interaction - Stefano Albrecht
Speaker: Dr Stefano V. Albrecht School of Informatics, University of Edinburgh Date: 20th October 2021 Title: Deep Reinforcement Learning for Multi-Agent Interaction Abstract: Our group specialises in developing machine learning algorithms, specifically deep reinforcement learning, to enable autonomous agents to interact with other agents (e.g. cooperate, compete) to solve a given task. Questions we tackle include: How can multiple agents learn together to solve a given task in a scalable and robust way? How can a single agent learn to collaborate effectively in a team in which other agents may have diverse types and may enter/leave at any time? I will also give a glimpse into our research with UK-based company Five AI on developing safe, interpretable planning and prediction algorithms for autonomous driving in urban environments.
Factored Value Functions for Cooperative MARL - Shimon Whiteson and Tabish Rashid
Why Choose Model-Based Reinforcement Learning?
I Gave ChatGPT a Body
Stefano V. Albrecht - From Deep Reinforcement Learning to LLM-based Agents
MIT 6.S191 (2019): Deep Reinforcement Learning
What do tech pioneers think about the AI revolution? - The Engineers, BBC World Service
Something is jamming GPS over Europe. Here's what we found
Reinforcement Learning for Agents - Will Brown, ML Researcher at Morgan Stanley
Introduction to Multi-Agent Reinforcement Learning
AlphaFold - The Most Useful Thing AI Has Ever Done
DeepMind / Turing Lecture: Learning how to learn efficiently - Nando de Freitas, DeepMind
This is not the AI we were promised | The Royal Society
An introduction to Policy Gradient methods - Deep Reinforcement Learning
Reinforcement Learning: Machine Learning Meets Control Theory
Yann LeCun Says LLMs Have 2 Years Left…
How can we add knowledge to AI agents?
Understanding Equilibria in Multi-Agent Systems - Michael Wooldridge, University of Oxford
Reinforcement Learning, by the Book
But what is a neural network? | Deep learning chapter 1
