Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/achronus/velora
A Reinforcement Learning (RL) framework for experimentation and learning RL concepts
https://github.com/achronus/velora
gymnasium model-free oop python pytorch reinforcement-learning
Last synced: 27 days ago
JSON representation
A Reinforcement Learning (RL) framework for experimentation and learning RL concepts
- Host: GitHub
- URL: https://github.com/achronus/velora
- Owner: Achronus
- License: mit
- Created: 2024-11-04T15:01:10.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-08T12:30:16.000Z (about 1 month ago)
- Last Synced: 2024-12-08T13:26:43.157Z (about 1 month ago)
- Topics: gymnasium, model-free, oop, python, pytorch, reinforcement-learning
- Language: Python
- Homepage:
- Size: 767 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Velora
**Velora** is a lightweight and extensible framework built on top of powerful libraries like `Gymnasium` (`Gym`) and `PyTorch`, specializing in model-free reinforcement learning (RL) algorithms. Designed to streamline RL experimentation, Velora offers PyTorch-based implementations of popular algorithms, simplifying the process of training and evaluating agents. Configuration management is enhanced with `Pydantic`, enabling seamless loading of settings from `YAML` files for a consistent, customizable user experience, and tested thoroughly using unit test packages like `pytest`. While Velora currently only supports `Gym` environments through a custom `EnvHandler`, its architecture is adaptable and expandable to other RL libraries.
## API Structure
The frameworks API is designed to be simple and intuitive. It's broken into two main categories: `core` and `extras`.
### Core
The primary building blocks you'll use regularly.
- `from velora import [controller], [enum]`
- `from velora.agent import [algorithm], [storage]`
- `from velora.env import [handler]`
### Extras
Extra items occassionally used under specific conditions.
- `from velora.exc import [error]`
- `from velora.utils import [method]`
## Framework Analysis
Velora's architecture adopts a robust, modular approach to Reinforcement Learning (RL) experimentation, designed for scalability and flexibility.
At the heart of the framework lies the `Controller` class which serves as the central hub for orchestrating the interactions between components.
The `EnvHandler` serves as an abstraction layer for managing various environment frameworks and handing environment interactions. Currently, it only supports [Gymnasium](https://gymnasium.farama.org/) with other options planned for the future.
The `Agent` class handles the agent's behaviour and learning process, managing the algorithm, policy updates, and experiences. It supports `PyTorch` models, optimizers, and loss functions for different strategies and algorithms.
The `Config` module simplifies hyperparameter tuning and setting implementation by reading from a single `YAML` file. By utilising `Pydantic`, settings are validated and applied automatically.
The `Analytics` module handles experiment tracking and logging, storing key performance metrics throughout the training process. Currently, it only supports [Weights and Biases](https://wandb.ai/) with other options planned for the future.