README.md

Example with Off-Policy Policy Learners

Description

We use synthetic bandit data to evaluate some off-policy learners using their ground-truth policy value calculable with synthetic data.

Evaluating Off-Policy Learners

In the following, we evaluate the performances of

• Uniform Random Policy (Random)
• Inverse Probability Weighting Policy Learner (IPWLearner)
• Policy Learner using Neural Networks (NNPolicyLearner)

See our documentation for the details about IPWLearner and NNPolicyLearner.

NNPolicyLearner can use the following OPE estimators as the objective function:

• Direct Method (DM)
• Inverse Probability Weighting (IPW)
• Doubly Robust (DR)

See our documentation for the details about these estimators.

Files

• ./evaluate_off_policy_learners.py implements the evaluation of off-policy learners using synthetic bandit data.
• ./conf/hyperparams.yaml defines hyperparameters of some ML methods used to define regression model and IPWLearner.

Scripts

# run evaluation of off-policy learners with synthetic bandit data
python evaluate_off_policy_learners.py\
    --n_rounds $n_rounds\
    --n_actions $n_actions\
    --dim_context $dim_context\
    --beta $beta\
    --base_model_for_evaluation_policy $base_model_for_evaluation_policy\
    --base_model_for_reg_model $base_model_for_reg_model\
    --off_policy_objective $off_policy_objective\
    --n_hidden $n_hidden\
    --n_layers $n_layers\
    --activation $activation\
    --solver $solver\
    --batch_size $batch_size\
    --early_stopping\
    --random_state $random_state

• $n_rounds and $n_actions specify the sample size and the number of actions of the synthetic bandit data, respectively.
• $dim_context specifies the dimension of context vectors.
• $beta specifies the inverse temperature parameter to control the behavior policy.
• $base_model_for_ipw_learner specifies the base ML model for defining evaluation policy and should be one of "logistic_regression", "random_forest", or "lightgbm".
• $off_policy_objective specifies the OPE estimator for NNPolicyLearner and should be one of "dm", "ipw", or "dr".
• $n_hidden specifies the size of hidden layers in NNPolicyLearner.
• $n_layers specifies the number of hidden layers in NNPolicyLearner.
• $activation specifies the activation function for NNPolicyLearner and should be one of "identity", "tanh", "logistic", or "relu".
• $solver specifies the optimizer for NNPolicyLearner and should be one of "adagrad", "sgd", or "adam".
• $batch_size specifies the batch size for NNPolicyLearner.
• $early_stopping enables early stopping of training of NNPolicyLearner.

For example, the following command compares the performance of the off-policy learners using synthetic bandit data with 100,00 rounds, 10 actions, five dimensional context vectors.

python evaluate_off_policy_learners.py\
    --n_rounds 10000\
    --n_actions 10\
    --dim_context 5\
    --base_model_for_ipw_learner logistic_regression\
    --off_policy_objective ipw\
    --n_hidden 100\
    --n_layers 1\
    --activation relu\
    --solver adam\
    --batch_size 200\
    --early_stopping

# policy values of off-policy learners (higher means better)
# =============================================
# random_state=12345
# ---------------------------------------------
#                               policy value
# random_policy                     0.499925
# ipw_learner                       0.782430
# nn_policy_learner (with ipw)      0.735947
# =============================================

The above result can change with different situations. You can try the evaluation with other experimental settings easily.