294 citations · 1k across the 10 of their papers we have counts for
11 papers
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Julien Perolat, Bart de Vylder, Daniel Hennes +31
We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few…
The Advantage Regret-Matching Actor-Critic
Audrūnas Gruslys, Marc Lanctot, Rémi Munos +10
Regret minimization has played a key role in online learning, equilibrium computation in games, and reinforcement learning (RL). In this paper, we describe a general model-free RL…
Navigating the Landscape of Multiplayer Games
Shayegan Omidshafiei, Karl Tuyls, Wojciech M. Czarnecki +9
Multiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence. Traditionally, researchers ha…
Neural Replicator Dynamics
Daniel Hennes, Dustin Morrill, Shayegan Omidshafiei +8
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environmen…
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents
Joel Z. Leibo, Cyprien de Masson d'Autume, Daniel Zoran +10
Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab (Beattie et al. 2016). Psychlab enables implementations of classical laboratory…
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys +5
To achieve general intelligence, agents must learn how to interact with others in a shared environment: this is the challenge of multiagent reinforcement learning (MARL). The simpl…