Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning

Markov games as a framework for multi-agent reinforcement learning

Michael L Littman. 1994 · 1994

Earlier work this paper cites.

Multiagent reinforcement learning: theoretical framework and an algorithm.. In Proceedings of the Fifteenth International Conference on Machine Learning , Vol. 98. Citeseer, 242–250

Junling Hu, Michael P Wellman, et al · 1998

Earlier work this paper cites.

On amount and quality of bias in reinforcement learning. In IEEE SMC’99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics , Vol. 2. IEEE, 728–733

G Hailu and G Sommer. 1999 · 1999

Earlier work this paper cites.

An algorithm for distributed reinforcement learning in cooperative multi-agent systems. In In Proceedings of the Seventeenth International Conference on Machine Learning . Citeseer

Martin Lauer and Martin Riedmiller. 2000 · 2000

Earlier work this paper cites.

Friend-or-Foe Q-Learning in General-Sum Games. In Proceedings of the Eighteenth International Conference on Machine Learning , Vol. 1. Morgan Kaufmann Publishers Inc., 322–328

Michael L. Littman. 2001 · 2001

Earlier work this paper cites.

Deterministic Policy Gradient Algorithms. In International Conference on Machine Learning , Vol. 32. PMLR, 387–395

David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller. 2014 · 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. 2016 · 2016

Cited alongside, same era.

Hindsight Experience Replay. In Advances in Neural Information Processing Systems . 5048–5058

Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba. 2017 · 2017

Cited alongside, same era.

Multi-agent actor-critic for mixed cooperative-competitive environments. In Advances in Neural Information Processing Systems . 6379–6390

Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch. 2017 · 2017

Cited alongside, same era.

Reinforcement learning: An introduction

Richard S Sutton and Andrew G Barto. 2018 · 2018

Cited alongside, same era.

Exploration with unreliable intrinsic reward in multi-agent reinforcement learning

Original

Wendelin Böhmer, Tabish Rashid, and Shimon Whiteson. 2019 · 2019

Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 4213–4220

Shihui Li, Yi Wu, Xinyue Cui, Honghua Dong, Fei Fang, and Stuart Russell. 2019 · 2019

Later among the works it cites.

Hindsight policy gradients

Paulo Rauber, Avinash Ummadisingu, Filipe Mutz, and Juergen Schmidhuber. 2019 · 2019

Later among the works it cites.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

Original

Julien Roy, Paul Barde, Félix G Harvey, Derek Nowrouzezahrai, and Christopher Pal. 2019 · 2019

Later among the works it cites.

Multi-Agent Actor-Critic with Hierarchical Graph Attention Network. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 7236–7243

Heechang Ryu, Hayong Shin, and Jinkyoo Park. 2020 · 2020

Later among the works it cites.

Influence-Based Multi-Agent Exploration