[HTML][HTML] Multi-agent reinforcement learning: A review of challenges and applications
In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
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 …
The surprising effectiveness of ppo in cooperative multi-agent games
Abstract Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …
[HTML][HTML] 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 …
Multi-agent reinforcement learning is a sequence modeling problem
Large sequence models (SM) such as GPT series and BERT have displayed outstanding
performance and generalization capabilities in natural language process, vision and …
performance and generalization capabilities in natural language process, vision and …
Qplex: Duplex dueling multi-agent q-learning
We explore value-based multi-agent reinforcement learning (MARL) in the popular
paradigm of centralized training with decentralized execution (CTDE). CTDE has an …
paradigm of centralized training with decentralized execution (CTDE). CTDE has an …
Ai alignment: A comprehensive survey
AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …
Is independent learning all you need in the starcraft multi-agent challenge?
Most recently developed approaches to cooperative multi-agent reinforcement learning in
the\emph {centralized training with decentralized execution} setting involve estimating a …
the\emph {centralized training with decentralized execution} setting involve estimating a …
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 …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
Towards autonomous multi-UAV wireless network: A survey of reinforcement learning-based approaches
Unmanned aerial vehicle (UAV)-based wireless networks have received increasing
research interest in recent years and are gradually being utilized in various aspects of our …
research interest in recent years and are gradually being utilized in various aspects of our …