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CoPS SIGNED

Coevolutionary Policy Search

Total Cost €

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EC-Contrib. €

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Partnership

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Project "CoPS" data sheet

The following table provides information about the project.

Coordinator
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD 

Organization address
address: WELLINGTON SQUARE UNIVERSITY OFFICES
city: OXFORD
postcode: OX1 2JD
website: www.ox.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 1˙480˙632 €
 EC max contribution 1˙480˙632 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2014-STG
 Funding Scheme ERC-STG
 Starting year 2015
 Duration (year-month-day) from 2015-10-01   to  2021-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD UK (OXFORD) coordinator 1˙480˙632.00

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 Project objective

I propose to develop a new class of decision-theoretic planning methods that overcome fundamental obstacles to the efficient optimization of autonomous agents. Creating agents that are effective in diverse settings is a key goal of artificial intelligence with enormous potential implications: robotic agents would be invaluable in homes, factories, and high-risk settings; software agents could revolutionize e-commerce, information retrieval, and traffic control. The main challenge lies in specifying an agent's policy: the behavioral strategy that determines its actions. Since the complexity of realistic tasks makes manual policy construction hopeless, there is great demand for decision-theoretic planning methods that automatically discover good policies. Despite enormous progress, the grand challenge of efficiently discovering effective policies for complex tasks remains unmet. A fundamental obstacle is the cost of policy evaluation: estimating a policy's quality by averaging performance over multiple trials. This cost grows quickly with increases in task complexity (making trials more expensive) or stochasticity (necessitating more trials). To address this difficulty, I propose a new approach that simultaneously optimizes both policies and the manner in which those policies are evaluated. The key insight is that, in many tasks, many trials are wasted because they do not elicit the controllable rare events critical for distinguishing between policies. Thus, I will develop methods that leverage coevolution to automatically discover the best events, instead of sampling them randomly. If successful, this project will greatly improve the efficiency of decision-theoretic planning and, in turn, help realize the potential of autonomous agents. In addition, by automatically identifying the most useful events, the resulting methods will help isolate critical factors in performance and thus yield new insights into what makes decision-theoretic problems hard.

 Publications

year authors and title journal last update
List of publications.
2018 Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, Shimon Whiteson
Counterfactual Multi-Agent Policy Gradients
published pages: , ISSN: , DOI:
2019-08-30
2018 Jakob Foerster‚ Richard Chen‚ Maruan Al−Shedivat‚ Shimon Whiteson‚ Pieter Abbeel and Igor Mordatch
Learning with Opponent−Learning Awareness
published pages: , ISSN: , DOI:
2019-08-30
2017 Jakob Foerster‚ Nantas Nardelli‚ Greg Farquhar‚ Phil Torr‚ Pushmeet Kohli and Shimon Whiteson
Stabilising Experience Replay for Deep Multi−Agent Reinforcement Learning
published pages: , ISSN: , DOI:
2019-08-30
2018 Gregory Farquhar‚ Tim Rocktaschel‚ Maximilian Igl and Shimon Whiteson
TreeQN and ATreeC: Differentiable Tree−Structured Models for Deep Reinforcement Learning
published pages: , ISSN: , DOI:
2019-08-30
2018 Kamil Ciosek Shimon Whiteson
Expected Policy Gradients
published pages: , ISSN: , DOI:
2019-08-30
2016 Jakob Foerster‚ Yannis Assael‚ Nando de Freitas and Shimon Whiteson
Learning to Communicate with Deep Multi−Agent Reinforcement Learning
published pages: , ISSN: , DOI:
2019-08-30
2017 Kamil Ciosek and Shimon Whiteson
OFFER: Off−Environment Reinforcement Learning
published pages: , ISSN: , DOI:
2019-08-30
2018 Supratik Paul‚ Konstantinos Chatzilygeroudis‚ Kamil Ciosek‚ Jean−Baptiste Mouret‚ Michael Osborne and Shimon Whiteson
Alternating Optimisation and Quadrature for Robust Control
published pages: , ISSN: , DOI:
2019-08-30
2018 Kyriacos Shiarlis‚ Markus Wulfmeier‚ Sasha Salter‚ Shimon Whiteson and Ingmar Posner
TACO: Learning Task Decomposition via Temporal Alignment for Control
published pages: , ISSN: , DOI:
2019-08-30
2018 Ciosek, Kamil; Whiteson, Shimon
Expected Policy Gradients for Reinforcement Learning
published pages: , ISSN: , DOI:
2 2019-08-30
2018 Rashid, Tabish; Samvelyan, Mikayel; de Witt, Christian Schroeder; Farquhar, Gregory; Foerster, Jakob; Whiteson, Shimon
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
published pages: , ISSN: , DOI:
2 2019-08-30
2018 Matthew Fellows‚ Kamil Ciosek and Shimon Whiteson
Fourier Policy Gradients
published pages: , ISSN: , DOI:
2019-08-30
2018 Foerster, Jakob; Farquhar, Gregory; Al-Shedivat, Maruan; Rocktäschel, Tim; Xing, Eric P.; Whiteson, Shimon
DiCE: The Infinitely Differentiable Monte-Carlo Estimator
published pages: , ISSN: , DOI:
2 2019-08-30
2018 Igl, Maximilian; Zintgraf, Luisa; Le, Tuan Anh; Wood, Frank; Whiteson, Shimon
Deep Variational Reinforcement Learning for POMDPs
published pages: , ISSN: , DOI:
1 2019-08-30

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