1 code implementation • 27 Feb 2023 • Minae Kwon, Sang Michael Xie, Kalesha Bullard, Dorsa Sadigh
During training, the LLM evaluates an RL agent's behavior against the desired behavior described by the prompt and outputs a corresponding reward signal.
no code implementations • 22 Sep 2022 • Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, SiQi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
no code implementations • 14 Mar 2021 • Kalesha Bullard, Douwe Kiela, Franziska Meier, Joelle Pineau, Jakob Foerster
In contrast, in this work, we present a novel problem setting and the Quasi-Equivalence Discovery (QED) algorithm that allows for zero-shot coordination (ZSC), i. e., discovering protocols that can generalize to independently trained agents.
no code implementations • 29 Oct 2020 • Kalesha Bullard, Franziska Meier, Douwe Kiela, Joelle Pineau, Jakob Foerster
Indeed, emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels.
no code implementations • 1 Jul 2019 • Kalesha Bullard, Yannick Schroecker, Sonia Chernova
Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives.