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shijazks/
ad-placement-rl-agents

Name: ad-placement-rl-agents
Availability: InStock

joblib

reinforcement-learning

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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

python

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.

shijazks/ad-placement-rl-agents