34 citations · 80 across the 15 of their papers we have counts for
3 papers · 1 filter
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,…
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt +9
Recent work in deep reinforcement learning (RL) has produced algorithms capable of mastering challenging games such as Go, chess, or shogi. In these works the RL agent directly obs…
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban
Peter Karkus, Mehdi Mirza, Arthur Guez +5
Intelligent robots need to achieve abstract objectives using concrete, spatiotemporally complex sensory information and motor control. Tabula rasa deep reinforcement learning (RL)…