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 estimated
elasticities.  Each experiment was repeated with different random
seeds: 10 seeds for the Open-Quadrant scenarios, and 5 seeds for the
Split-World scenarios. For each individual experiment, we
provide:  Training histories (e.g. equality and productivity
throughout training) the phase 1 and phase 2 configuration
files,  40 episode dense logs (the final 10 simulation logs
across 4 environment replicas), phase 1 final agent models, and phase 2
final agent and planner models. Finally, we include all data and results
used to calibrate the Saez elasticity estimates and to estimate elasticity
directly from a sweep over flat-rate tax policies: training histories, the
phase 1 and phase 2 configuration files,  phase 1 final agent
models, and phase 2 final agent and planner models.