249 citations · 249 across the 1 of their papers we have counts for
4 papers
The Hanabi Challenge: A New Frontier for AI Research
Nolan Bard, Jakob N. Foerster, Sarath Chandar +12
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made…
Malthusian Reinforcement Learning
Joel Z. Leibo, Julien Perolat, Edward Hughes +6
Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked popu…
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
Jakob N. Foerster, Francis Song, Edward Hughes +5
When observing the actions of others, humans make inferences about why they acted as they did, and what this implies about the world; humans also use the fact that their actions wi…
Population Based Training of Neural Networks
Max Jaderberg, Valentin Dalibard, Simon Osindero +9
Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model a…