58 citations · 215 across the 22 of their papers we have counts for
6 papers · 1 filter
Difference Rewards Policy Gradients
Jacopo Castellini, Sam Devlin, Frans A. Oliehoek +1
Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of…
"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games
Mikhail Jacob, Sam Devlin, Katja Hofmann
Game agents such as opponents, non-player characters, and teammates are central to player experiences in many modern games. As the landscape of AI techniques used in the games indu…
Meta-Learning Divergences of Variational Inference
Ruqi Zhang, Yingzhen Li, Christopher De Sa +2
Variational inference (VI) plays an essential role in approximate Bayesian inference due to its computational efficiency and broad applicability. Crucial to the performance of VI i…
A Comparison of Self-Play Algorithms Under a Generalized Framework
Daniel Hernandez, Kevin Denamganai, Sam Devlin +2
Throughout scientific history, overarching theoretical frameworks have allowed researchers to grow beyond personal intuitions and culturally biased theories. They allow to verify a…
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
Rika Antonova, Maksim Maydanskiy, Danica Kragic +2
We address the problem of learning reusable state representations from streaming high-dimensional observations. This is important for areas like Reinforcement Learning (RL), which…
Rolling Horizon Evolutionary Algorithms for General Video Game Playing
Raluca D. Gaina, Sam Devlin, Simon M. Lucas +1
Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolutionary Algorithms, have recently managed to beat the state of the art in win rate across many video games.…