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 …
Self-play fine-tuning converts weak language models to strong language models
Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …
The AI Economist: Taxation policy design via two-level deep multiagent reinforcement learning
Artificial intelligence (AI) and reinforcement learning (RL) have improved many areas but are
not yet widely adopted in economic policy design, mechanism design, or economics at …
not yet widely adopted in economic policy design, mechanism design, or economics at …
Language agents with reinforcement learning for strategic play in the werewolf game
Agents built with large language models (LLMs) have recently achieved great
advancements. However, most of the efforts focus on single-agent or cooperative settings …
advancements. However, most of the efforts focus on single-agent or cooperative settings …
Distributed deep reinforcement learning: A survey and a multi-player multi-agent learning toolbox
With the breakthrough of AlphaGo, deep reinforcement learning has become a recognized
technique for solving sequential decision-making problems. Despite its reputation, data …
technique for solving sequential decision-making problems. Despite its reputation, data …
Modelling behavioural diversity for learning in open-ended games
Promoting behavioural diversity is critical for solving games with non-transitive dynamics
where strategic cycles exist, and there is no consistent winner (eg, Rock-Paper-Scissors) …
where strategic cycles exist, and there is no consistent winner (eg, Rock-Paper-Scissors) …
Pipeline psro: A scalable approach for finding approximate nash equilibria in large games
Finding approximate Nash equilibria in zero-sum imperfect-information games is
challenging when the number of information states is large. Policy Space Response Oracles …
challenging when the number of information states is large. Policy Space Response Oracles …
Team-PSRO for learning approximate TMECor in large team games via cooperative reinforcement learning
Recent algorithms have achieved superhuman performance at a number of two-player zero-
sum games such as poker and go. However, many real-world situations are multi-player …
sum games such as poker and go. However, many real-world situations are multi-player …
Multi-agent training beyond zero-sum with correlated equilibrium meta-solvers
Two-player, constant-sum games are well studied in the literature, but there has been limited
progress outside of this setting. We propose Joint Policy-Space Response Oracles (JPSRO) …
progress outside of this setting. We propose Joint Policy-Space Response Oracles (JPSRO) …
Strategic knowledge transfer
In the course of playing or solving a game, it is common to face a series of changing other-
agent strategies. These strategies often share elements: the set of possible policies to play …
agent strategies. These strategies often share elements: the set of possible policies to play …