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 …
Evolutionary dynamics of multi-agent learning: A survey
The interaction of multiple autonomous agents gives rise to highly dynamic and
nondeterministic environments, contributing to the complexity in applications such as …
nondeterministic environments, contributing to the complexity in applications such as …
Put your money where your mouth is: Evaluating strategic planning and execution of llm agents in an auction arena
Recent advancements in Large Language Models (LLMs) showcase advanced reasoning,
yet NLP evaluations often depend on static benchmarks. Evaluating this necessitates …
yet NLP evaluations often depend on static benchmarks. Evaluating this necessitates …
Incentive policies for prefabrication implementation of real estate enterprises: An evolutionary game theory-based analysis
J Wang, Y Qin, J Zhou - Energy Policy, 2021 - Elsevier
Prefabrication construction method has been considered an effective way for enhancing the
environmental performance and sustainable development of the construction industry. Many …
environmental performance and sustainable development of the construction industry. Many …
Multiagent learning: Basics, challenges, and prospects
Multiagent systems (MAS) are widely accepted as an important method for solving problems
of a distributed nature. A key to the success of MAS is efficient and effective multiagent …
of a distributed nature. A key to the success of MAS is efficient and effective multiagent …
Autonomous algorithmic collusion: Q‐learning under sequential pricing
T Klein - The RAND Journal of Economics, 2021 - Wiley Online Library
Prices are increasingly set by algorithms. One concern is that intelligent algorithms may
learn to collude on higher prices even in the absence of the kind of coordination necessary …
learn to collude on higher prices even in the absence of the kind of coordination necessary …
[HTML][HTML] A research on promoting chemical fertiliser reduction for sustainable agriculture purposes: Evolutionary game analyses involving 'government, farmers, and …
M Tian, Y Zheng, X Sun, H Zheng - Ecological Indicators, 2022 - Elsevier
The excessive use of chemical fertilisers seriously worsened the agro-ecological
environment in China. Despite the Chinese government enacted a series of policies to …
environment in China. Despite the Chinese government enacted a series of policies to …
Learning automata-based multiagent reinforcement learning for optimization of cooperative tasks
Z Zhang, D Wang, J Gao - IEEE transactions on neural …, 2020 - ieeexplore.ieee.org
Multiagent reinforcement learning (MARL) has been extensively used in many applications
for its tractable implementation and task distribution. Learning automata, which can be …
for its tractable implementation and task distribution. Learning automata, which can be …
α-Rank: Multi-Agent Evaluation by Evolution
We introduce α-Rank, a principled evolutionary dynamics methodology, for the evaluation
and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical …
and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical …
A generalized training approach for multiagent learning
This paper investigates a population-based training regime based on game-theoretic
principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense …
principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense …