Taming Reinforcement Learning Chaos: An MLOps Architecture for Experiment Management

Reinforcement learning (RL) has achieved superhuman performance in domains ranging from Go (AlphaGo) to complex robotics control. However, unlike supervised learning, where data is static, RL is dynamic. It relies on an agent interacting with an environment through massive trial and error.

For engineering teams, this "trial and error" nature creates a significant MLOps bottleneck. A single viable model might require hundreds of experiments, each with slight variations in reward functions, learning rates, or environment physics.