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Exploring large language model based intelligent agents: Definitions, methods, and prospects
When can we learn general-sum Markov games with a large number of players sample-efficiently?
Multi-agent reinforcement learning has made substantial empirical progresses in solving
games with a large number of players. However, theoretically, the best known sample …
games with a large number of players. However, theoretically, the best known sample …
Independent policy gradient for large-scale markov potential games: Sharper rates, function approximation, and game-agnostic convergence
We examine global non-asymptotic convergence properties of policy gradient methods for
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …
Deep reinforcement learning: Emerging trends in macroeconomics and future prospects
T Atashbar, RA Shi - 2022 - books.google.com
The application of Deep Reinforcement Learning (DRL) in economics has been an area of
active research in recent years. A number of recent works have shown how deep …
active research in recent years. A number of recent works have shown how deep …
Provably fast convergence of independent natural policy gradient for markov potential games
This work studies an independent natural policy gradient (NPG) algorithm for the multi-agent
reinforcement learning problem in Markov potential games. It is shown that, under mild …
reinforcement learning problem in Markov potential games. It is shown that, under mild …
Simulating human society with large language model agents: City, social media, and economic system
This tutorial will delve into the fascinating realm of simulating human society using Large
Language Model (LLM)-driven agents, exploring their applications in cities, social media …
Language Model (LLM)-driven agents, exploring their applications in cities, social media …
Taxai: A dynamic economic simulator and benchmark for multi-agent reinforcement learning
Taxation and government spending are crucial tools for governments to promote economic
growth and maintain social equity. However, the difficulty in accurately predicting the …
growth and maintain social equity. However, the difficulty in accurately predicting the …
An analysis of the ingredients for learning interpretable symbolic regression models with human-in-the-loop and genetic programming
Interpretability is a critical aspect to ensure a fair and responsible use of machine learning
(ML) in high-stakes applications. Genetic programming (GP) has been used to obtain …
(ML) in high-stakes applications. Genetic programming (GP) has been used to obtain …