Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms

This paper analyzes canonical design and evaluation paradigms in deep reinforcement learning. It develops theoretical foundations for reinforcement-learning scaling laws and argues that algorithm rankings need not vary monotonically across data regimes. Large-scale experiments reportedly show that a line of research using conventional paradigms reached incorrect conclusions. The work therefore examines how scaling, model capacity, and algorithmic complexity affect conclusions about deep RL performance. The supplied abstract does not provide the benchmark suite, algorithms, statistical procedures, or detailed numerical results.