Details of Grant 

EPSRC Reference:	EP/V024868/1
Title:	Turing AI Fellowship: Advancing Multi-Agent Deep Reinforcement Learning for Sequential Decision Making in Real-World Applications
Principal Investigator:	Montana, Professor G
Other Investigators:
Researcher Co-Investigators:
Project Partners:	Columbia University Eurocontrol GEFCO UK Ltd Hong Kong Polytechnic University Hong Kong University of Science and Tech Imperial College London Indian Inst of Technology Kharagpur Inovo Robotics Insignia Medical Systems King Abdullah University of Sci and Tech Kings College London Kinova Europe GmbH Manchester University NHS Fdn Trust nVIDIA Soliton IT Limited Stanford University The Engineering Laboratory of the United The Shadow Robot Company TU Wien Univ Hosp Coventry and Warwick NHS Trust University Hospitals Birmingham NHS FT University of Cambridge
Department:	WMG
Organisation:	University of Warwick
Scheme:	EPSRC Fellowship - NHFP
Starts:	01 January 2021	Ends:	31 December 2025	Value (£):	1,518,509
EPSRC Research Topic Classifications:	Artificial Intelligence
EPSRC Industrial Sector Classifications:	Manufacturing Healthcare
Related Grants:
Panel History:	Panel Date Panel Name Outcome 06 Oct 2020 Turing AI Acceleration Fellowship Interview Panel B Announced
Summary on Grant Application Form
Despite being far from having reached 'artificial general intelligence' - the broad and deep capability for a machine to comprehend our surroundings - progress has been made in the last few years towards a more specialised AI: the ability to effectively address well-defined, specific goals in a given environment, which is the kind of task-oriented intelligence that is part of many human jobs. Much of this progress has been enabled by deep reinforcement learning (DRL), one of the most promising and fast-growing areas within machine learning. In DRL, an autonomous decision maker - the "agent" - learns how to make optimal decisions that will eventually lead to reaching a final goal. DRL holds the promise of enabling autonomous systems to learn large repertoires of collaborative and adaptive behavioural skills without human intervention, with application in a range of settings from simple games to industrial process automation to modelling human learning and cognition. Many real-world applications are characterised by the interplay of multiple decision-makers that operate in the same shared-resources environment and need to accomplish goals cooperatively. For instance, some of the most advanced industrial multi-agent systems in the world today are assembly lines and warehouse management systems. Whether the agents are robots, autonomous vehicles or clinical decision-makers, there is a strong desire for and increasing commercial interest in these systems: they are attractive because they can operate on their own in the world, alongside humans, under realistic constraints (e.g. guided by only partial information and with limited communication bandwidth). This research programme will extend the DRL methodology to systems comprising of many interacting agents that must cooperatively achieve a common goal: multi-agent DRL, or MADRL.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Organisation Website:	http://www.warwick.ac.uk