BatchContextualEpsilonGreedyPolicy | Batch Contextual Epsilon-Greedy Policy |
BatchContextualLinTSPolicy | Batch Contextual Thompson Sampling Policy |
BatchLinUCBDisjointPolicyEpsilon | Batch Disjoint LinUCB Policy with Epsilon-Greedy |
ContextualLinearBandit | Contextual Linear Bandit Environment |
cram_bandit | Cram Bandit: On-policy Statistical Evaluation in Contextual Bandits |
cram_bandit_est | Cram Bandit Policy Value Estimate |
cram_bandit_sim | Cram Bandit Simulation |
cram_bandit_var | Cram Bandit Variance of the Policy Value Estimate |
cram_estimator | Cram Policy Estimator for Policy Value Difference (Delta) |
cram_expected_loss | Cram ML Expected Loss Estimate |
cram_learning | Cram Policy Learning |
cram_ml | Cram ML: Simultaneous Machine Learning and Evaluation |
cram_policy | Cram Policy: Efficient Simultaneous Policy Learning and Evaluation |
cram_policy_value_estimator | Cram Policy: Estimator for Policy Value (Psi) |
cram_simulation | Cram Policy Simulation |
cram_variance_estimator | Cram Policy: Variance Estimate of the crammed Policy Value Difference (Delta) |
cram_variance_estimator_policy_value | Cram Policy: Variance Estimate of the crammed Policy Value estimate (Psi) |
cram_var_expected_loss | Cram ML: Variance Estimate of the crammed expected loss estimate |
fit_model | Cram Policy: Fit Model |
fit_model_ml | Cram ML: Fit Model ML |
get_betas | Generate Reward Parameters for Simulated Linear Bandits |
LinUCBDisjointPolicyEpsilon | LinUCB Disjoint Policy with Epsilon-Greedy Exploration |
ml_learning | Cram ML: Generalized ML Learning |
model_predict | Cram Policy: Predict with the Specified Model |
model_predict_ml | Cram ML: Predict with the Specified Model |
set_model | Cram Policy: Set Model |
test_baseline_policy | Validate or Set the Baseline Policy |
test_batch | Validate or Generate Batch Assignments |
validate_params | Cram Policy: Validate User-Provided Parameters for a Model |
validate_params_fnn | Cram Policy: Validate Parameters for Feedforward Neural Networks (FNNs) |