Multi-agent reinforcement learning: A selective overview of theories and algorithms

K Zhang, Z Yang, T Başar - Handbook of reinforcement learning and …, 2021 - Springer
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications

TT Nguyen, ND Nguyen… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Reinforcement learning (RL) algorithms have been around for decades and employed to
solve various sequential decision-making problems. These algorithms, however, have faced …

Multi-agent deep reinforcement learning: a survey

S Gronauer, K Diepold - Artificial Intelligence Review, 2022 - Springer
The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

Actor-attention-critic for multi-agent reinforcement learning

S Iqbal, F Sha - International conference on machine …, 2019 - proceedings.mlr.press
Reinforcement learning in multi-agent scenarios is important for real-world applications but
presents challenges beyond those seen in single-agent settings. We present an actor-critic …

A survey and critique of multiagent deep reinforcement learning

P Hernandez-Leal, B Kartal, ME Taylor - Autonomous Agents and Multi …, 2019 - Springer
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Mean field multi-agent reinforcement learning

Y Yang, R Luo, M Li, M Zhou… - … on machine learning, 2018 - proceedings.mlr.press
Existing multi-agent reinforcement learning methods are limited typically to a small number
of agents. When the agent number increases largely, the learning becomes intractable due …

Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension

M Joshi, E Choi, DS Weld, L Zettlemoyer - arXiv preprint arXiv:1705.03551, 2017 - arxiv.org
We present TriviaQA, a challenging reading comprehension dataset containing over 650K
question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by …

A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications

W Du, S Ding - Artificial Intelligence Review, 2021 - Springer
Deep reinforcement learning has proved to be a fruitful method in various tasks in the field of
artificial intelligence during the last several years. Recent works have focused on deep …

Mastering complex control in moba games with deep reinforcement learning

D Ye, Z Liu, M Sun, B Shi, P Zhao, H Wu, H Yu… - Proceedings of the AAAI …, 2020 - aaai.org
We study the reinforcement learning problem of complex action control in the Multi-player
Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state …

A unified game-theoretic approach to multiagent reinforcement learning

M Lanctot, V Zambaldi, A Gruslys… - Advances in neural …, 2017 - proceedings.neurips.cc
There has been a resurgence of interest in multiagent reinforcement learning (MARL), due
partly to the recent success of deep neural networks. The simplest form of MARL is …