semvec/pypi-packages huggingface dataset:
drl-wizard 0.1.3
Deep Reinforcement Learning training platform
License: MIT
Requires Python: >=3.9

# DRL Wizard

**DRL Wizard** is a clean, modular Deep Reinforcement Learning toolkit for training, comparing, and understanding modern RL algorithms. It supports multiple environments, multiple algorithms, real-time monitoring, and a unified workflow through a FastAPI backend and Streamlit UI.

---

## 🚀 Features

- **Algorithms:** PPO, TRPO, DQN, Double DQN, Dueling DQN, SAC (more coming)
- **Environments:** Gymnasium, Atari (ALE), image-based & multi-discrete action spaces
- **Architecture:** FastAPI backend, Streamlit UI, SQLAlchemy storage, Pydantic configs
- **Experiment Tools:** concurrent jobs, graceful stop, NDJSON logs, TensorBoard, job archives
- **Extensible:** easy to add algorithms, envs, or visualization components

---

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## 📦 Installation
Basic
```bash
pip install drl-wizard
```

UI + Dev tools:
```bash
pip install drl-wizard[ui,dev]
```

## 🖥️ Running
 - **Running the UI & Backend:**
```bash
drl-wizard-run
```

 - **Running Backend:**
```bash
drl-wizard-api
```

 - **Running UI:**
```bash
drl-wizard-ui
```

## 📝 Notes
- drl-wizard-run launches the full platform.

- Extras ([ui], [dev]) include Streamlit, and development dependencies.
rf-faiss 1.13.0rc1
A library for efficient similarity search and clustering of dense vectors
License: MIT
Keywords: search nearest neighbors


Faiss is a library for efficient similarity search and clustering of dense
vectors. It contains algorithms that search in sets of vectors of any size,
up to ones that possibly do not fit in RAM. It also contains supporting
code for evaluation and parameter tuning. Faiss is written in C++ with
complete wrappers for Python/numpy. Some of the most useful algorithms
are implemented on the GPU. It is developed by Facebook AI Research.