An overview of multi-agent reinforcement learning from game theoretical perspective

Y Yang, J Wang - arXiv preprint arXiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Deep multiagent reinforcement learning: Challenges and directions

A Wong, T Bäck, AV Kononova, A Plaat - Artificial Intelligence Review, 2023 - Springer
This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

Multi-agent reinforcement learning is a sequence modeling problem

M Wen, J Kuba, R Lin, W Zhang… - Advances in …, 2022 - proceedings.neurips.cc
Large sequence models (SM) such as GPT series and BERT have displayed outstanding
performance and generalization capabilities in natural language process, vision and …

Rode: Learning roles to decompose multi-agent tasks

T Wang, T Gupta, A Mahajan, B Peng… - arXiv preprint arXiv …, 2020 - arxiv.org
Role-based learning holds the promise of achieving scalable multi-agent learning by
decomposing complex tasks using roles. However, it is largely unclear how to efficiently …

Smarts: An open-source scalable multi-agent rl training school for autonomous driving

M Zhou, J Luo, J Villella, Y Yang… - … on robot learning, 2021 - proceedings.mlr.press
Interaction is fundamental in autonomous driving (AD). Despite more than a decade of
intensive R&D in AD, how to dynamically interact with diverse road users in various contexts …

Celebrating diversity in shared multi-agent reinforcement learning

C Li, T Wang, C Wu, Q Zhao… - Advances in Neural …, 2021 - proceedings.neurips.cc
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve
complex cooperative tasks. Its success is partly because of parameter sharing among …

Consensus of multi-agent systems via fully distributed event-triggered control

X Li, Y Tang, HR Karimi - Automatica, 2020 - Elsevier
This article studies consensus of linear multi-agent systems (MASs) on undirected graphs.
An adaptive event-triggering protocol is constructed for consensus control by using relative …

Adaptive event-triggered consensus of multiagent systems on directed graphs

X Li, Z Sun, Y Tang, HR Karimi - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
This article systematically studies consensus of linear multiagent systems (MASs) on
directed graphs through adaptive event-triggered control. It presents innovative adaptive …

Roma: Multi-agent reinforcement learning with emergent roles

T Wang, H Dong, V Lesser, C Zhang - arXiv preprint arXiv:2003.08039, 2020 - arxiv.org
The role concept provides a useful tool to design and understand complex multi-agent
systems, which allows agents with a similar role to share similar behaviors. However …

Dop: Off-policy multi-agent decomposed policy gradients

Y Wang, B Han, T Wang, H Dong… - … conference on learning …, 2020 - openreview.net
Multi-agent policy gradient (MAPG) methods recently witness vigorous progress. However,
there is a significant performance discrepancy between MAPG methods and state-of-the-art …