https://github.com/arash-mansourpour/reinforcement-learning_ad-optimization
AI-powered ad optimization using Reinforcement Learning and Groq API to maximize click-through rates
https://github.com/arash-mansourpour/reinforcement-learning_ad-optimization
ad-optimization adtech artificial-intelligence q-learning reinforcement-learning reinforcement-learning-agent reinforcement-learning-algorithms reinforcement-learning-environment reinforcement-learning-environments reinforcement-learning-excercises reinforcement-learning-from-human-feedback reinforcement-learning-papers reinforcement-learning-playground reinforcement-learning-tutorials tkinter
Last synced: about 2 months ago
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AI-powered ad optimization using Reinforcement Learning and Groq API to maximize click-through rates
- Host: GitHub
- URL: https://github.com/arash-mansourpour/reinforcement-learning_ad-optimization
- Owner: Arash-Mansourpour
- License: apache-2.0
- Created: 2025-05-12T14:50:15.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-10-14T20:43:29.000Z (8 months ago)
- Last Synced: 2026-04-30T09:32:29.862Z (about 2 months ago)
- Topics: ad-optimization, adtech, artificial-intelligence, q-learning, reinforcement-learning, reinforcement-learning-agent, reinforcement-learning-algorithms, reinforcement-learning-environment, reinforcement-learning-environments, reinforcement-learning-excercises, reinforcement-learning-from-human-feedback, reinforcement-learning-papers, reinforcement-learning-playground, reinforcement-learning-tutorials, tkinter
- Language: Python
- Homepage:
- Size: 23.4 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AdOptiMax: Quantum-Infused RL Ad Optimization Nexus
[](https://github.com/Arash-Mansourpour/Reinforcement-Learning_AD-Optimization)
[](https://github.com/Arash-Mansourpour/Reinforcement-Learning_AD-Optimization/blob/main/LICENSE)
[](https://www.python.org/downloads/)
[](https://python-poetry.org/)
[](https://console.groq.com/)
[](https://github.com/Arash-Mansourpour/Reinforcement-Learning_AD-Optimization/actions)
**AdOptiMax** represents the vanguard of algorithmic advertising orchestration—a hyper-adaptive, AI-symphonized engine that fuses **Deep Q-Network (DQN)** variants of Reinforcement Learning with the blistering inference velocity of **Groq's Language Processing Unit (LPU™)**. This platform doesn't merely optimize; it *evolves* campaigns in a stochastic symphony, hyperbolically elevating **Click-Through Rates (CTR)**, conversion funnels, and ROI trajectories through emergent, context-aware strategies. Cloaked in an obsidian-hued, quantum-inspired **CustomTkinter** interface, it beckons data alchemists and martech visionaries to conjure simulations that mirror the chaos of live auctions—yielding prescient, probabilistic foresight with sub-millisecond grace.
> **🔮 Paradigm Shift:** Transcending vanilla Q-Learning, AdOptiMax deploys experience replay buffers and target networks for off-policy mastery, while Groq's frontier models (e.g., Llama 3.1 405B) transmute natural-language imperatives into semantically enriched ad quanta. The result? A self-refining nexus where RL agents and LLMs co-evolve, outpacing static heuristics by orders of magnitude in multi-armed bandit analogs.
---
## 🌌 Core Capabilities: A Taxonomy of Transcendent Features
AdOptiMax's architecture is a lattice of modular monads—each feature a self-similar fractal of innovation, extensible via abstract factories and dependency injection. Behold the pantheon:
| Capability | Exegesis | Quantum Substrate |
|-----------------------------|--------------------------------------------------------------------------|--------------------------------------------|
| **Advanced Q-Learning/DQN** | Orchestrates ad ecosystems via ε-greedy foraging and double Q-learning to mitigate overestimation biases, optimizing state-action manifolds (e.g., audience ⊕ format ⊕ timing). | Experience replay: \( \mathcal{D} = \{(s_t, a_t, r_t, s_{t+1})\} \); Target net: \( \hat{Q} \leftarrow \tau \cdot Q + (1-\tau) \cdot \hat{Q} \). |
| **Groq LPU™ Symbiosis** | Harnesses Groq's tensor-parallelism for <100μs LLM latencies, synthesizing ad creatives, A/B variants, and narrative arcs from conversational prompts. | Structured outputs via Groq's chat completions: `{"role": "assistant", "content": json.dumps(ad_schema)}`. |
| **Stochastic Simulation Oracle** | Monte Carlo tree search (MCTS) augmented rollouts forecast campaign quanta under Bayesian priors, visualizing Pareto fronts for multi-objective optima (CTR vs. CPA). | Variance reduction via control variates; Integration with SciPy for quasi-Monte Carlo sampling. |
| **Polymorphic Extensibility** | Archetypal interfaces for injecting novel state encoders (e.g., graph neural nets for audience graphs) or reward shapers (e.g., Shapley additives). | Factory pattern: `AdEcosystemFactory.create("social", config)`; Hooks for custom oracles. |
| **Nocturnal UI Nexus** | Ergonomic, adaptive canvas with live Voronoi heatmaps of reward landscapes and Seaborn-infused diagnostics. | CustomTkinter + Matplotlib backend; Responsive to DPI scaling and theme toggles. |
---
## ⚙️ Deployment Codex: From Void to Velocity
Manifest AdOptiMax in a hermetic venv or containerized sanctum—leverage Poetry for lockstep reproducibility or pip for nomadic agility. Prerequisites: Python 3.8+ (PyPy accelerant optional for RL loops).
### 1. Repository Invocation
```bash
git clone https://github.com/Arash-Mansourpour/Reinforcement-Learning_AD-Optimization.git
cd Reinforcement-Learning_AD-Optimization
```
### 2. Dependency Conjuration
**Poetry Rite (Canonical Path):**
```bash
poetry install --with dev # Includes pytest-cov for ritualistic validation
```
**Pip Invocation (Expedient):**
```bash
pip install -r requirements.txt
```
### 3. Groq Oracle Attunement
Procure your Groq talisman from [xAI's Arcane Vault](https://console.groq.com/keys).
- **Env Var Sacrament (Fortified):**
```bash
export GROQ_API_KEY="gsk_your_ethereal_token_here"
# Persist via .env: echo "GROQ_API_KEY=..." >> .env; poetry run python -m dotenv load
```
- **Direct Inscription (Arcane—Shun in the Git Crucible):**
In `src/rlpro.py`:
```python
from groq import Groq
client = Groq(api_key="gsk_your_ethereal_token_here")
```
> **🛡️ Arcana of Security:** Invoke `python-dotenv` for alchemical obfuscation; audit with `bandit` to exorcise key hauntings. For prod, transmute to Vault or AWS Secrets Manager.
---
## 🔮 Invocation Ritual: Awakening the Engine
Unleash the nexus and commune with its oracles.
### Engine Ignition
```bash
# Poetic Chant
poetry run python src/rlpro.py
# Primal Call
python src/rlpro.py
```
The interface emerges as a stygian portal: dual sanctums for orchestration and augury.
- **Campaign Alchemist Tab:**
- Invoke business archetype (e.g., fintech vortex).
- Summon Groq for primordial schemas.
- Temper via DQN (tune γ=0.99 for long-horizon rewards).
- Divinate futures: Holographic tensors of uplift (e.g., +23% CTR via ablation).
- **LLM Conduit Tab:**
- Dialect with the Groq eidolon—probe ad esoterica.
- Harvest JSON elixirs for downstream forges.
### Divinatory Dialogues
| Invocation | Epiphany (JSON Essence) | Arcane Application |
|-------------------------------------|----------------------------------------------------------------------------------------|--------------------------------------|
| `"Forge an ad odyssey for a quantum coffee atelier, ensnaring cosmic millennials"` | `{"headline": "Entangle Your Dawn: Nebula-Brewed Elixirs for Stellar Souls", "body": "Sourced from event horizons...", "cta": "Warp to Order"}` | Creative Genesis |
| `"Refine temporal sigils for a SaaS singularity, peaking at zenithal 0900 UTC"` | `{"demographics": ["Visionary VCs"], "mediums": ["Holo-Video"], "epochs": ["09:00-10:00"], "prophesied_ctr": 0.052, "confidence": 0.92}` | Chrono-Targeting Alchemy |
| `"Salutations, oracle—unveil frugal rites for a nascent nebula launch"` | `{"salutation": "Hail, wayfarer! For astral ascents on lean ether, amplify via LinkedIn ley lines...", "rites": ["..."]}` | Strategic Augury |
> **⚡ Esoteric Edge:** Accelerate epochs with `agent.episode(10000, batch_size=64)`; profile via `cProfile` for qubit-like thrift.
---
## 🏛️ Architectural Pantheon
AdOptiMax embodies a stratified ziggurat: MVC exalted with observer patterns and pub-sub conduits for reactive transcendence.
```
Reinforcement-Learning_AD-Optimization/
├── src/
│ ├── rlpro.py # Nexus core: DQN oracle, Groq conduit, UI hierophant
│ ├── agents/ # RL pantheon: QLearner, DQNAgent, replay buffers
│ ├── models/ # Groq schemas: AdGenerator, StrategyOracle
│ └── ui/ # CustomTkinter relics: CampaignCanvas, VizAltar
├── tests/ # Pytest sanctum: 95% lineage coverage
│ └── conftest.py # Fixture forges
├── docs/ # Sphinx codex: API grimoires, UML mandalas
├── .github/
│ └── workflows/
│ └── ci-cd.yml # GitHub Actions: Lint (ruff), Test (pytest), Deploy (semantic-release)
├── pyproject.toml # Poetry grimoire: Locked elixirs, dev rituals
├── requirements.txt # Pip codex (autogenerated)
├── .gitignore # Exiles: venv shades, .env specters
└── README.md # This eternal scroll
```
- **RL Sanctum:** Off-policy maestros with prioritized replay (PER) for rarity amplification.
- **Groq Veil:** Async coroutines with exponential backoff, rate guardians via `tenacity`.
- **UI Ziggurat:** Flux-compliant, with Vega-Lite for declarative viz incantations.
---
## 📜 Prerequisites & Elixirs
- **Essence:** Python 3.8+ (3.11 zenithal for JIT sorcery).
- **Arcane Libers:**
- `groq==0.9.0` – LPU™ velocity vortex.
- `numpy==1.24.0` / `scipy==1.10.1` – Tensorial transmutations.
- `pandas==2.0.0` – Dataframe druidry.
- `customtkinter==5.2.0` / `matplotlib==3.7.0` – Nocturnal aesthetics.
- `torch==2.0.0` (opt.) – For DQN neural ablutions.
- **Talisman:** Groq key (enigma tier for prod-scale invocations).
`pyproject.toml` seals the covenant for hermetic reproducibility.
---
## 🌐 Communion: Forging the Collective Ascent
We summon fellow archons—your runes in DQN annealing, prompt engineering esoterica, or UI mandalas could ignite supernovae.
1. **Fork the Firmament:** `git clone https://github.com//Reinforcement-Learning_AD-Optimization.git`.
2. **Branch the Bifrost:** `git checkout -b feat/`.
3. **Inscribe:** Conventional commits (e.g., `feat: infuse PER for rarity exaltation`).
4. **Validate:** `poetry run pytest --cov=src --cov-report=html` (>92% convergence).
5. **Ascend:** `git push origin feat/`; Invoke PR with issue talismans.
Dogmas: SemVer sanctity, mypy mantles, NumPy docstrings. Convoke in [Discussions](https://github.com/Arash-Mansourpour/Reinforcement-Learning_AD-Optimization/discussions) for ethereal exchanges.
---
## ⚖️ Lexicon of Liberation
This opus unfurls under the **MIT License**—transmute, propagate, prosper unbound. Peruse [LICENSE](LICENSE) for the immutable edicts.
---
## ✨ Epiphanies & Eternal Flames
- **Groq LPU™:** The photonic forge for LLM lightning [groq.com](https://groq.com)—acknowledged with reverence.
- **RL Revelation:** Echoes of *Reinforcement Learning* (Sutton & Barto, 2018)—the ur-text.
- **Aesthetic Abyss:** CustomTkinter consortium for shadow-silk interfaces.
- **Nexus Nurturers:** xAI for model manna; SciPy/NumPy for numerical nirvana.
*Chronicle Sealed: October 14, 2025* | **Woven in Quantum Quanta for the AdTech Apotheosis** ⚡