RLlib for Deep Hierarchical Multiagent Reinforcement Learning

Reinforcement learning (RL) is an effective method for solving problems that require agents to learn the best way to act in complex environments. RLlib is a powerful tool for applying reinforcement learning to problems where there are multiple agents or when agents must take on multiple roles. There are many of resources for learning about RLlib from a theoretical or academic perspective, but there is a lack of materials for learning how to use RLlib to solve your own practical problems. This tutorial helps to fill that gap. If you want to get right into RLlib, fell free to skip to the next section. Thorndike observed that some behaviors in animals arise from a gradual stamping in [Thorndike, 1898].