Adaptive Discretization in Reinforcement Learning Performance guarantees for RL algorithms are typically worst case instances, which pathological by design and not observed meaningful applications. Moreover, many domains (such as computer systems networking applications) have large state-action spaces require to execute with low latency. This phenomenon highlights a trifecta of goals practical ...