Paper accepted at ICLR 2018

Our paper “TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning” has been accepted at #ICLR2018 ! Congratulations to Gregory Farquhar, Tim Rocktäschel, Maximilian Igl and Shimon Whiteson.

WhiRL members have two papers accepted at AAMAS 2018

We’re happy to announce that WhiRL members have two accepted papers at #AAMAS2018 this year.Learning with Opponent-Learning Awareness Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch. Arxiv version: Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making, Luisa M Zintgraf, Diederik M Roijers, Sjoerd Linders, Catholijn M Jonker, Ann Nowé ( )   

Outstanding Student Paper at AAAI-18

Our paper “Counterfactual Multi−Agent Policy Gradients” by Jakob Foerster‚ Gregory Farquhar‚ Triantafyllos Afouras‚ Nantas Nardelli and Shimon Whiteson has been selected as the Outstanding Student Paper for AAAI-18! Congratulations to all authors!

WhiRL members have three papers accepted at AAAI

Whiteson Research Lab members have three papers accepted at AAAI-2018: Counterfactual Multi-Agent Policy GradientsJakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, Shimon WhitesonAlternating Optimisation and Quadrature for Robust ControlSupratik Paul, Shimon Whiteson, Michael Osborne, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste MouretExpected Policy GradientsKamil Ciosek, Shimon Whiteson

WhiRL members have three papers accepted at NIPS

Whiteson Research Lab members have three papers accepted at NIPS-2017: Utile Context Tree Weighting João Messias and Shimon Whiteson   End-to-end Differentiable Proving Tim Rocktäschel and Sebastian Riedel   Cortical Microcircuits as Gated-Recurrent Neural Networks Rui Ponte Costa, Yannis Assael, Brendan Shillingford, Nando de Freitas, Tim Vogels

Three ICML papers from WhiRL members.

WhiRL members have contributed three papers to this year’s ICML: Stabilising Experience Replay for Deep Multi−Agent Reinforcement Learning Programming with a Differentiable Forth Interpreter Input Switched Affine Networks: An RNN Architecture Designed for Interpretability