Christian Schroeder de Witt
Christian Schroeder de Witt
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Deep Multi-Agent Reinforcement Learning
FACMAC: Factored Multi-Agent Centralised Policy Gradients
We propose multi-agent common knowledge reinforcement learning (MACKRL).
Bei Peng
,
Tabish Rashid
,
Christian Schroeder de Witt
,
Pierre-Alexandre Kamienny
,
Philip Torr
,
Wendelin Boehmer
,
Shimon Whiteson
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Multi-Agent Common Knowledge Reinforcement Learning
We propose multi-agent common knowledge reinforcement learning (MACKRL).
Christian Schroeder de Witt
,
Jakob N. Foerster
,
Gregory Farquhar
,
Philip H. S. Torr
,
Wendelin Boehmer
,
Shimon Whiteson
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Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem
We suggest treating Stratospheric Aersol Injection (SAI) as a high-dimensional control problem, with policies trained according to a context-sensitive reward function within the Deep Reinforcement Learning (DRL) paradigm.
Christian Schroeder de Witt
,
Thomas Hornigold
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Hijacking Malaria Simulators with Probabilistic Programming
We introduce an approach that allows one to treat a large class of population-based epidemiology simulators as probabilistic generative models.
Bradley Gram-Hansen
,
Christian Schroeder de Witt
,
Tom Rainforth
,
Philip HS Torr
,
Yee Whye Teh
,
Atılım Güneş Baydin
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The Starcraft Multi-Agent Challenge
We propose the StarCraft Multi-Agent Challenge (SMAC) to measure real progress in MARL.
Mikayel Samvelyan
,
Tabish Rashid
,
Christian Schroeder de Witt
,
Gregory Farquhar
,
Nantas Nardelli
,
Tim GJ Rudner
,
Chia-Man Hung
,
Philip HS Torr
,
Jakob Foerster
,
Shimon Whiteson
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Biological Evolution and Genetic Algorithms: Exploring the Space of Abstract Tile Self-Assembly
A physically-motivated genetic algorithm (GA) and full enumeration for a tile-based model of self-assembly (JaTAM) is implemented using a graphics processing unit (GPU).
Christian Schroeder de Witt
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