Exploring large language model based intelligent agents: Definitions, methods, and prospects

Y Cheng, C Zhang, Z Zhang, X Meng, S Hong… - ar** and mitigating misaligned models
A Pan, K Bhatia, J Steinhardt - arxiv preprint arxiv:2201.03544, 2022 - arxiv.org
Reward hacking--where RL agents exploit gaps in misspecified reward functions--has been
widely observed, but not yet systematically studied. To understand how reward hacking …

When can we learn general-sum Markov games with a large number of players sample-efficiently?

Z Song, S Mei, Y Bai - arxiv preprint arxiv:2110.04184, 2021 - arxiv.org
Multi-agent reinforcement learning has made substantial empirical progresses in solving
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

D Ding, CY Wei, K Zhang… - … Conference on Machine …, 2022 - proceedings.mlr.press
We examine global non-asymptotic convergence properties of policy gradient methods for
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 …

Provably fast convergence of independent natural policy gradient for markov potential games

Y Sun, T Liu, R Zhou, PR Kumar… - Advances in Neural …, 2023 - proceedings.neurips.cc
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 …

Simulating human society with large language model agents: City, social media, and economic system

C Gao, F Xu, X Chen, X Wang, X He, Y Li - Companion Proceedings of …, 2024 - dl.acm.org
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 …

Taxai: A dynamic economic simulator and benchmark for multi-agent reinforcement learning

Q Mi, S **a, Y Song, H Zhang, S Zhu… - arxiv preprint arxiv …, 2023 - arxiv.org
Taxation and government spending are crucial tools for governments to promote economic
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

G Nadizar, L Rovito, A De Lorenzo, E Medvet… - ACM Transactions on …, 2024 - dl.acm.org
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 …