Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms
The paper argues that standard deep RL evaluation can rank algorithms wrongly across data regimes.
Ezgi Korkmaz frames the result around scaling, capacity, and complexity in reinforcement learning. The paper says asymptotic performance does not have a monotone relationship with rankings measured under different data regimes. Its large-scale experiments are presented as evidence that canonical design and evaluation practices led part of the field to incorrect conclusions. HF Daily Papers' note
Ezgi Korkmaz frames the result around scaling, capacity, and complexity in reinforcement learning. The paper says asymptotic performance does not have a monotone relationship with rankings measured under different data regimes. Its large-scale experiments are presented as evidence that canonical design and evaluation practices led part of the field to incorrect conclusions. HF Daily Papers' note
score 4