Representation and Invariance in Reinforcement Learning
The paper 'Representation and Invariance in Reinforcement Learning' examines whether the formal setup of an RL system affects agent intelligence. The authors demonstrate that four common RL frameworks are not mathematically equivalent, meaning converting agents between setups does not always preserve relative intelligence. This requires researchers to specify their exact framework before drawing conclusions.