Work

Version v2.0.0 — Reinforced-lib: Rapid prototyping of reinforcement learning solutions

Year: 2025

Type: software-paper

Abstract: Reinforced-lib is a Python library for rapid development of reinforcement learning (RL) solutions, designed with user-friendliness and extensibility in mind. Built on JAX, it supports both deep RL and traditional agents, providing a rich ecosystem and comprehensive documentation. This release introduces several major enhancements: support for vectorized environments, implementation of proximal policy optimization for discrete and continuous action spaces, and integration of neuroevolution algorithms as an alternative to RL approaches. Additional improvements include a masked multi-armed bandit (MAB) agent, new schedulers with an MAB interface, documentation updates, and bug fixes.

Source: SoftwareX

DOI: 10.1016/j.softx.2025.102400

Author: Maksymilian Wojnar

Institution: AGH University of Krakow

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funder: Narodowe Centrum Nauki