137 research outputs found
UAV Swarm-Enabled Localization in Isolated Region: A Rigidity-constrained Deployment Perspective
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 dynamic strategies of diverse self-interested
households presents a challenge for governments to implement effective tax
policies. Given its proficiency in modeling other agents in partially
observable environments and adaptively learning to find optimal policies,
Multi-Agent Reinforcement Learning (MARL) is highly suitable for solving
dynamic games between the government and numerous households. Although MARL
shows more potential than traditional methods such as the genetic algorithm and
dynamic programming, there is a lack of large-scale multi-agent reinforcement
learning economic simulators. Therefore, we propose a MARL environment, named
\textbf{TaxAI}, for dynamic games involving households, government, firms,
and financial intermediaries based on the Bewley-Aiyagari economic model. Our
study benchmarks 2 traditional economic methods with 7 MARL methods on TaxAI,
demonstrating the effectiveness and superiority of MARL algorithms. Moreover,
TaxAI's scalability in simulating dynamic interactions between the government
and 10,000 households, coupled with real-data calibration, grants it a
substantial improvement in scale and reality over existing simulators.
Therefore, TaxAI is the most realistic economic simulator, which aims to
generate feasible recommendations for governments and individuals.Comment: 26 pages, 8 figures, 12 table
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