State aggregation minimizing Q error (SAmQ) is a toolkit to conduct state aggregation for dynamic discrete choice model estimation.
pip install git+https://github.com/gengsinong/SAmQ.git@master
The main function is in main.py. It includes a list of parameters.
python main.py
The tunning is conducted by submit_wandb_airline.sh,
submit_wandb_bus_engine.sh
and submit_wandb_bus_engine_plot.sh.
This file uses wandb.
To generate the decision-making data for dynamic discrete choice model estimation, we consider a synthetic setting similar to the bus engine application in the Rust model.
The specific setting is defined and detialed in env.bus_env.
Then, we conduct soft-q iteration to estimate the optimal choice-specific value function, and generate data in rl.softQ.
Experiments on airline market entry analysis leverages the data in airline_data.
The state aggregation step is conducted in irl.pqr.
The aggregation uses the deep PQR method.