Fetching the paper…
Reading the bibliography…
In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint action space that grows exponentially with the number of agents.
On the theory of the brownian motion
George E Uhlenbeck and Leonard S Ornstein · 1930
Earlier work this paper cites.
Christopher JCH Watkins and Peter Dayan · 1992
Earlier work this paper cites.
Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
Earlier work this paper cites.
Reinforcement learning: A survey
Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore · 1996
Earlier work this paper cites.
Intrinsically motivated reinforcement learning
Nuttapong Chentanez, Andrew G Barto, and Satinder P Singh · 2005
Earlier work this paper cites.
Opponent modeling in real-time strategy games
Frederik Schadd, Sander Bakkes, and Pieter Spronck · 2007
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, and Shimon Whiteson · 2016
Cited alongside, same era.
Opponent modeling in deep reinforcement learning
He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daumé III · 2016
Cited alongside, same era.
Multi-agent cooperation and the emergence of (natural) language
Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni · 2016
Cited alongside, same era.
The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
Cited alongside, same era.
Cooperative multi-agent control using deep reinforcement learning
Jayesh K. Gupta, Maxim Egorov, and Mykel J. Kochenderfer · 2017
Cited alongside, same era.
Learning attentional communication for multi-agent cooperation
Jiechuan Jiang and Zongqing Lu · 2018
Later among the works it cites.
Emergence of grounded compositional language in multi-agent populations
Igor Mordatch and Pieter Abbeel · 2018
Later among the works it cites.
Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
Later among the works it cites.
Learning to share and hide intentions using information regularization
Daniel Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, and David J Schwab · 2018
Later among the works it cites.
Feudal multi-agent hierarchies for cooperative reinforcement learning
Sanjeevan Ahilan and Peter Dayan · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, and Chun-Yi Lee · 2017
Cited alongside, same era.
Categorical reparametrization with gumble-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
Cited alongside, same era.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
Cited alongside, same era.
Measuring collaborative emergent behavior in multi-agent reinforcement learning
Sean L Barton, Nicholas R Waytowich, Erin Zaroukian, and Derrik E Asher · 2018
Cited alongside, same era.
Counterfactual multi-agent policy gradients
Jakob N Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
Cited alongside, same era.
Is multiagent deep reinforcement learning the answer or the question? a brief survey
Pablo Hernandez-Leal, Bilal Kartal, and Matthew E Taylor · 2018
Cited alongside, same era.
Bayesian action decoder for deep multi-agent reinforcement learning
Jakob Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, and Michael Bowling · 2019
Closest in time.
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning
Pablo Hernandez-Leal, Bilal Kartal, and Matthew E. Taylor · 2019
Closest in time.
Actor-attention-critic for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2019
Closest in time.
Social influence as intrinsic motivation for multi-agent deep reinforcement learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro Ortega, Dj Strouse, Joel Z Leibo, and Nando De Freitas · 2019
Closest in time.
Google research football: A novel reinforcement learning environment
Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, et al · 2019
Closest in time.
Maven: Multi-agent variational exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, and Shimon Whiteson · 2019
Closest in time.