S. Whiteson

Publications
Influence

Counterfactual Multi-Agent Policy Gradients

Jakob N. Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, S. Whiteson
Computer Science
AAAI
24 May 2017

We propose a new multi-agent actor-critic method called counterfactual multi- agent (COMA) policy gradients. Expand

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid, Mikayel Samvelyan, C. S. Witt, Gregory Farquhar, Jakob N. Foerster, S. Whiteson
Computer Science, Mathematics
ICML
30 March 2018

We propose QMIX, a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. Expand

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

Jakob N. Foerster, Yannis M. Assael, N. D. Freitas, S. Whiteson
Computer Science
NIPS
1 May 2016

We introduce new environments for studying the learning of communication protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. Expand

Learning with Opponent-Learning Awareness

Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, S. Whiteson, P. Abbeel, Igor Mordatch
Computer Science
AAMAS
13 September 2017

We present Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in the environment. Expand

A theoretical and empirical analysis of Expected Sarsa

H. V. Seijen, H. V. Hasselt, S. Whiteson, M. Wiering
Computer Science
IEEE Symposium on Adaptive Dynamic Programming…
15 May 2009

Expected Sarsa exploits knowledge about stochasticity in the behavior policy to perform updates with lower variance. Expand

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

Jakob N. Foerster, Nantas Nardelli, +4 authors S. Whiteson
Computer Science
ICML
28 February 2017

This paper proposes two methods that address this problem: 1) using a multi-agent variant of importance sampling to naturally decay obsolete data and conditioning each agent's value function on a fingerprint that disambiguates the age of the data sampled from the replay memory. Expand

The StarCraft Multi-Agent Challenge

Mikayel Samvelyan, Tabish Rashid, +7 authors S. Whiteson
Computer Science, Mathematics
AAMAS
11 February 2019

We propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap. Expand

A Survey of Multi-Objective Sequential Decision-Making

Diederik M. Roijers, P. Vamplew, S. Whiteson, R. Dazeley
Mathematics, Computer Science
J. Artif. Intell. Res.
1 October 2013

Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning. Expand

LipNet: End-to-End Sentence-level Lipreading

Yannis M. Assael, Brendan Shillingford, S. Whiteson, N. D. Freitas
Computer Science
5 November 2016

We present LipNet, a model that maps a variable-length sequence of video frames to text, making use of spatiotemporal convolutions, recurrent network, and the connectionist temporal classification loss, trained entirely end toend. Expand

Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem

Masrour Zoghi, S. Whiteson, Rémi Munos, M. Rijke
Mathematics, Computer Science
ICML
11 December 2013

We propose and analyze a new algorithm, called Relative Upper Confidence Bound (RUCB), for the K-armed dueling bandit problem (Yue et al., 2012), where the feedback comes in the form of pairwise preferences. Expand

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