Position: Deployed Reinforcement Learning should be Continual

This position paper argues that deployed Reinforcement Learning (RL) agents should engage in continual learning rather than a train-then-fix paradigm. It identifies four sources of non-stationarity post-deployment, highlighting the necessity for agents to continuously adapt to achieve optimal performance in real-world scenarios.