Introduction

RL-A2A stands for Reinforcement Learning – Actor-to-Actor. This repository implements advanced reinforcement learning algorithms with a focus on Actor-to-Actor architectures. It is designed for both researchers and practitioners to experiment with state-of-the-art RL methods.

Key Features

• Modular and extensible codebase for RL research.
• Support for multiple RL algorithms (e.g., A2A, A2C, PPO, etc.).
• Flexible environment integration (OpenAI Gym, custom environments).
• Tools for logging, visualization, and experiment management.

Target Audience

• Beginners: Learn about RL concepts and implement working agents.
• Experts: Rapidly prototype new algorithms and run large-scale experiments.

Why RL-A2A?

RL-A2A provides a clean, understandable, and customizable codebase, filling the gap between educational repositories and production RL frameworks.