HelloRL: A modular framework, like Lego for Reinforcement Learning (github.com)

HelloRL has launched a groundbreaking modular framework designed to simplify Reinforcement Learning (RL) for practitioners. By allowing users to easily swap between various RL algorithms—such as Actor Critic, A2C, and PPO—through a single train function, HelloRL eliminates the common headaches associated with maintaining different implementations. Users can mix and match over 20 modular components, from actors and critics to learning rates and advantage methods, making experimentation much more efficient and less error-prone. This initiative is significant for the AI/ML community as it democratizes access to complex RL techniques, enabling researchers to quickly build and test custom algorithms without being bogged down by integration issues. Built in Python and compatible with PyTorch, HelloRL also offers real-time training progress updates and supports remote execution via Modal, facilitating high-performance training runs. This innovative approach not only accelerates development but also enhances reproducibility in RL research, pointing towards a future where building on previous work is faster and more intuitive.