AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing Optimization

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A Customizable Benchmark Environment for Reinforcement Learning Algorithms in Search Engine Marketing (SEM)

Description

AdCraft is a Python-based framework designed as a customizable benchmark for evaluating the performance of reinforcement learning (RL) algorithms in the field of search engine marketing (SEM). This repository provides a comprehensive set of tools and resources, including a package of Gymnasium environments, to simulate SEM campaigns and assess the efficacy of RL algorithms across various auction scenarios.

Features

Built-in baseline algorithm for reference and performance comparison
Seamless integration with popular RL algorithms: Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Twin Delayed Deep Deterministic Policy Gradient (TD3)
Customizable environment parameters including keyword volume, click-through rate (CTR), and conversion rate (CVR)
Evaluation metrics tailored for SEM, such as Average Keyword Normalized Cumulative Profit (AKNCP) and Normalized Conversion Profit (NCP)
Support for studying environmental sparsity and non-stationarity, enabling exploration of real-world SEM dynamics
Flexibility to implement and study non-stationary dynamics, reflecting real-world scenarios
Package of Gymnasium environments for different auction scenarios

Installation

NOTE: To install you need to have Rust installed. Instructions are here: https://www.rust-lang.org/tools/install

Easiest method is to pip install git+https://github.com/Mikata-Project/adcraft.git

Alternate method is to clone repo and make install.

Usage

After installing the required dependencies, you can begin exploring AdCraft by running the provided Jupyter notebooks. To run these notebooks, ensure that Jupyter Notebook and the AdCraft package are installed. By following these steps, you can efficiently utilize AdCraft's tools within the Jupyter Notebook environment.

Install Jupyter notebook

pip install notebook

Running the RL agent training notebook

Then, we can run the RL agent traing notebook by navigating to the notebook directory and launching a notebook.

cd adcraft/RL

jupyter train_agent.ipynb

Example usage with RLlib on GPU

from adcraft.gymnasium_kw_env import BiddingSimulation
import ray
from ray.rllib.algorithms.ppo import PPOConfig

ray.init(
    num_gpus=1,
    ignore_reinit_error=True,
    log_to_driver=False,
    logging_level="WARNING",
    include_dashboard=False
)

config = (
    PPOConfig()
    .framework("torch")
    .rollouts(create_env_on_local_worker=True)
    .debugging(seed=0)
    .training(model={"fcnet_hiddens" : [32,32]}) #shape of policy observation to action
    .environment(env = BiddingSimulation)
    .resources(num_gpus=1)
)

ppo = config.build()

for _ in range(1):
    ppo.train()

eval_dict = ppo.evaluate()
eval_dict

Tests

Lint code with: make lint

Run tests with: make test

License

This project is licensed under the Apache-2.0 license.

GiovanniChiurco/adcraft