activity
20162026
most citedDeep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability

180 citations · 578 across the 40 of their papers we have counts for

collaborators
Showing cs.AIShow all

5 papers · 1 filter

cs.AI2022★ 153 cited

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…

cs.AI2021★ 12 cited

From Motor Control to Team Play in Simulated Humanoid Football

Siqi Liu, Guy Lever, Zhe Wang +19

Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous mus…

cs.AI2020

Game Plan: What AI can do for Football, and What Football can do for AI

Karl Tuyls, Shayegan Omidshafiei, Paul Muller +33

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball,…

cs.AI2020★ 22 cited

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…

cs.AI2017

Crossmodal Attentive Skill Learner

Shayegan Omidshafiei, Dong-Ki Kim, Jason Pazis +1

This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 20…