Web Ad-Placement RL Agents
Multiple RL/bandit models trained on a synthetic web ad-placement dataset: [link to your dataset repo].
Models included
| File | Algorithm | Type |
|---|---|---|
BC.pt | Behavior Cloning | Imitation learning baseline |
CQL.pt | Conservative Q-Learning | Offline RL |
FQI.joblib | Fitted Q-Iteration | Offline RL |
LinUCB.joblib | LinUCB | Contextual bandit |
surrogate_bc.joblib | Surrogate reward model for BC | Evaluation/simulation |
surrogate_cql.joblib | Surrogate reward model for CQL | Evaluation/simulation |
surrogate_fqi.joblib | Surrogate reward model for FQI | Evaluation/simulation |
surrogate_linucb.joblib | Surrogate reward model for LinUCB | Evaluation/simulation |
action_encoder.joblib | Action space encoder | Preprocessing |
feature_encoders.joblib | Feature encoders | Preprocessing |
feature_columns.joblib | Feature column schema | Preprocessing |
Usage
import joblib
import torch
# example: loading LinUCB
linucb_model = joblib.load("LinUCB.joblib")
# example: loading a PyTorch model
bc_model = torch.load("BC.pt")
# load supporting encoders
feature_encoders = joblib.load("feature_encoders.joblib")
action_encoder = joblib.load("action_encoder.joblib")
Training data
Trained on synthetic data — see [dataset link] for details on generation and schema.
Limitations
Trained entirely on synthetic data; not validated on real user traffic or production ad-serving environments.