README.md

Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits

This repo contains the code for the empirical evaluation in the paper Pessimism: Offline Policy Optimization In Contextual Bandits.
We implement several offline policy optimization methods with inverse probability weighting and doubly robust estimators, policy-gradient-based and linear-regression-based cost-sensitive classification oracles, pseudo loss and sample variance regularizers.

Create environment

Make sure conda is installed. Run

conda env create -f environment.yml
source activate cb-learn

Choose experimental setting for discrete-action experiments in

./scripts_discrete/exp_params.py

Prepare data for discrete-action experiments

Run

python ./scripts_discrete/prepare_data.py

Simulate bandit feedback data for discrete-action experiments

On a cluster with Slurm workload manager, run

python ./scripts_discrete/run_simulate_bandit_feedback.py

Offline policy learning with different methods for the discrete-action experiments

On a cluster with Slurm workload manager, run

python ./scripts_discrete/run_OPO.py

Model selection for the discrete-action experiments

Run

python ./scripts_discrete/model_selection.py

Generate figure and tables for the discrete-action experiments

Run

python ./scripts_discrete/plot_improvement_figure.py
python ./scripts_discrete/generate_table.py
python ./scripts_discrete/transform_table.py

Choose experimental setting for continuous-action experiments in

./scripts_continuous/exp_params.py

Prepare data for continuous-action experiments

Run

python ./scripts_continuous/prepare_data.py

Simulate bandit feedback data for continuous-action experiments

On a cluster with Slurm workload manager, run

python ./scripts_continuous/run_simulate_bandit_feedback.py

Offline policy learning with different methods for the continuous-action experiments

On a cluster with Slurm workload manager, run

python ./scripts_continuous/run_OPO.py

Model selection for the continuous-action experiments

Run

python ./scripts_continuous/model_selection.py

Generate figure and tables for the continuous-action experiments

Run

python ./scripts_continuous/plot_improvement_figure.py
python ./scripts_continuous/generate_table.py
python ./scripts_continuous/transform_table.py