发现深度强化学习的难度主要由表示方式决定，传统指标失效。

Principled evaluation is critical for progress in deep reinforcement learning (RL), yet it lags behind the theory-driven benchmarks of tabular RL. While tabular settings benefit from well-understood hardness measures like MDP diameter and suboptimality gaps, deep RL benchmarks are often chosen based on intuition and popularity. This raises a critical question: can tabular hardness metrics be adapted to guide non-tabular benchmarking? We investigate this question and reveal a fundamental gap. Our primary contribution is demonstrating that the difficulty of non-tabular environments is dominated by a factor that tabular metrics ignore: representation hardness. The same underlying MDP can pose vastly different challenges depending on whether the agent receives state vectors or pixel-based observations. To enable this analysis, we introduce \texttt{pharos}, a new open-source library for principled RL benchmarking that allows for systematic control over both environment structure and agent representations. Our extensive case study using \texttt{pharos} shows that while tabular metrics offer some insight, they are poor predictors of deep RL agent performance on their own. This work highlights the urgent need for new, representation-aware hardness measures and positions \texttt{pharos} as a key tool for developing them.