RLlib

RLlib is an open-source library for reinforcement learning (RL) that offers support for production-level, highly distributed RL workloads. It maintains unified and simple APIs for a wide range of industry applications. With RLlib, you can train agents in various setups, such as multi-agent, from offline datasets, or using externally connected simulators. It is already used in production by industry leaders in multiple verticals. RLlib does not automatically install a deep-learning framework but supports TensorFlow and PyTorch. To get started, you need to install the necessary packages. You can follow an example of running a PPO Algorithm on the Taxi domain. RLlib also supports different environments and algorithms. Its key features include highly distributed learning, multi-agent RL, external simulators, offline RL, and customization options. Users can customize various aspects of the training and experimental workflows. Overall, RLlib provides a powerful tool for reinforcement learning in different scenarios.