2 papers
cs.LG2026
Position: Deployed Reinforcement Learning should be Continual
Parnian Behdin, Kevin Roice, Golnaz Mesbahi
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…
cs.LG2025
Position: Lifetime tuning is incompatible with continual reinforcement learning
Golnaz Mesbahi, Parham Mohammad Panahi, Olya Mastikhina +3
In continual RL we want agents capable of never-ending learning, and yet our evaluation methodologies do not reflect this. The standard practice in RL is to assume unfettered acces…