Learning to Share and Hide Intentions using Information Regularization

Markov games as a framework for multi-agent reinforcement learning

Michael L Littman · 1994

Earlier work this paper cites.

Algorithms for inverse reinforcement learning

Andrew Y Ng, Stuart J Russell, et al · 2000

Earlier work this paper cites.

Policy gradient methods for reinforcement learning with function approximation

Richard S Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour · 2000

Earlier work this paper cites.

Friend-or-foe q-learning in general-sum games

Michael L Littman · 2001

Earlier work this paper cites.

Apprenticeship learning via inverse reinforcement learning

Pieter Abbeel and Andrew Y Ng · 2004

Earlier work this paper cites.

Understanding and sharing intentions: The origins of cultural cognition

Michael Tomasello, Malinda Carpenter, Josep Call, Tanya Behne, and Henrike Moll · 2005

Earlier work this paper cites.

Action understanding as inverse planning

Chris L Baker, Rebecca Saxe, and Joshua B Tenenbaum · 2009

Earlier work this paper cites.

Help or hinder: Bayesian models of social goal inference

Tomer Ullman, Chris Baker, Owen Macindoe, Owain Evans, Noah Goodman, and Joshua B. Tenenbaum · 2009

Earlier work this paper cites.

Legibility and predictability of robot motion

Anca D. Dragan, Kenton C.T. Lee, and Siddhartha S. Srinivasa · 2013

Earlier work this paper cites.

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014

Earlier work this paper cites.

A rational account of pedagogical reasoning: Teaching by, and learning from, examples

Patrick Shafto, Noah D Goodman, and Thomas L Griffiths · 2014

Cited alongside, same era.

Tensorflow: A system for large-scale machine learning

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016

Cited alongside, same era.

Unifying count-based exploration and intrinsic motivation

Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016

Cited alongside, same era.

Cooperative inverse reinforcement learning

Dylan Hadfield-Menell, Stuart J Russell, Pieter Abbeel, and Anca Dragan · 2016

Cited alongside, same era.

Showing versus doing: Teaching by demonstration

Mark K Ho, Michael Littman, James MacGlashan, Fiery Cushman, and Joseph L Austerweil · 2016

Cited alongside, same era.

Count-based exploration with neural density models

Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, and Rémi Munos · 2017

Later among the works it cites.

Distral: Robust multitask reinforcement learning

Original

Yee Whye Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017

Later among the works it cites.

Learning with opponent-learning awareness

Jakob Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, and Igor Mordatch · 2018

Closest in time.

Inequity aversion improves cooperation in intertemporal social dilemmas

Edward Hughes, Joel Z Leibo, Matthew Phillips, Karl Tuyls, Edgar Dueñez Guzman, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin McKee, Raphael Koster, Heather Roff, and Thore Graepel · 2018

Closest in time.

Intrinsic social motivation via causal influence in multi-agent RL

Original

Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, and Nando de Freitas · 2018