Position: Deployed Reinforcement Learning should be Continual
Reinforcement Learning (RL) has received increasing attention and adoption in real-world use cases. Most of these systems follow a train-then-fix paradigm, where trained agents do not learn while interacting with the world until performance degrades and retraining becomes necessary. In this position paper, we argue that deploying an agent that is incapable of optimality, but receives an evaluative reward signal, is inherently a continual RL problem. We identify four sources of non-stationarity after deployment that necessitate never-ending learning, and highlight why the best deployed agents never stop adapting. We analyze successful examples of continual RL in the real world, and present...
| Authors | Parnian Behdin, Kevin Roice, Golnaz Mesbahi |
|---|---|
| Primary category | cs.LG |
| Published | 1 June 2026 |
| Last updated at source | 6 June 2026 |
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