The AI Economist: Taxation policy design via two-level deep reinforcement learning

This dataset contains all raw experimental data for the paper \"The AI Economist: Taxation Policy Design via Two-level Deep Multi-Agent Reinforcement Learning\". 

The accompanying simulation, reinforcement learning, and data visualization code can be found at https://github.com/salesforce/ai-economist.

For the one-step economy experiments, we provide:

training histories,

configuration files (these experiments do not use phases), and

final agent and planner models.

For the Gather-Trade-Build scenario, the data covers 6 spatial layouts: two Open-Quadrant (with 4 and 10 agents), and four Split-World maps with different configurations of the high-skilled and low-skilled agents. It also covers 4 tax policies (the AI Economist, Saez, free-market, and US federal). In addition, the AI Economist has been optimized for two social welfare functions: the product of equality and productivity, and inverse-income weighted utility. The Saez tax policy also uses esti...