Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications
Reinforcement learning (RL) algorithms have been around for decades and employed to
solve various sequential decision-making problems. These algorithms, however, have faced …
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 …
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Actor-attention-critic for multi-agent reinforcement learning
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 …
presents challenges beyond those seen in single-agent settings. We present an actor-critic …
A survey and critique of multiagent deep reinforcement learning
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 …
led to a dramatic increase in the number of applications and methods. Recent works have …
Mean field multi-agent reinforcement learning
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 …
of agents. When the agent number increases largely, the learning becomes intractable due …
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
We present TriviaQA, a challenging reading comprehension dataset containing over 650K
question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by …
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 …
artificial intelligence during the last several years. Recent works have focused on deep …
Mastering complex control in moba games with deep reinforcement learning
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 …
Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state …
A unified game-theoretic approach to multiagent reinforcement learning
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 …
partly to the recent success of deep neural networks. The simplest form of MARL is …