Understanding Average Reward RL

Average reward reinforcement learning (RL) is a mathematical formulation which differs from traditional methods by focusing on continuous decision-making scenarios. In this context, a continuing problem represents a situation where the agent interacts with the environment indefinitely, without any kind of time-outs or resets. The discussion highlights how average reward RL is particularly relevant for scenarios such as resource allocation on servers or autonomous taxi management, where decisions need to be made continuously based on evolving circumstances. This leads to complex challenges in decision-making that must factor in various parameters, such as request prioritization and future outcomes.