#Self-supervised

AI-Optimizer provides a wide array of algorithm libraries for reinforcement learning, covering both model-free and model-based methods. It is designed for single-agent and multi-agent setups, featuring a distributed framework for optimized training efficiency. Highlighted areas of innovation include solutions for multiagent reinforcement learning, offline RL, self-supervised learning, and model-based RL, tackling issues such as scalability and sample efficiency. This tool is a valuable asset for researchers and practitioners, offering accessible implementations suitable for complex real-world scenarios.