## Legend: Algorithm Comparison ### Overview The image is a horizontal legend from a chart or graph, displaying seven distinct colored lines, each paired with a text label. This legend is used to identify different algorithms or methods, likely in a comparative performance study within fields such as reinforcement learning or optimization. ### Components/Axes The legend consists of seven colored line segments, each followed by a text label. The elements are arranged horizontally from left to right. There are no axes, scales, or numerical data present in this isolated image. ### Detailed Analysis The legend contains the following color-label pairs, listed in order from left to right: 1. **Color:** Teal (dark cyan) line. **Label:** `Rainbow` 2. **Color:** Green line. **Label:** `PPO` 3. **Color:** Orange line. **Label:** `PPO-Lagrangian` 4. **Color:** Yellow line. **Label:** `KCAC` 5. **Color:** Cyan (light blue) line. **Label:** `RC-PPO` 6. **Color:** Magenta (pink-purple) line. **Label:** `PLPG` 7. **Color:** Red line. **Label:** `NSAM(ours)` All text is in English. The label `NSAM(ours)` explicitly denotes the method proposed by the authors of the document from which this legend is taken. ### Key Observations * The legend uses a distinct, high-contrast color palette to ensure clear differentiation between the seven data series. * The inclusion of `(ours)` on the final label is a common academic convention to highlight the authors' contribution in a comparative figure. * The labels suggest a focus on reinforcement learning algorithms (e.g., PPO, Rainbow) and constrained optimization methods (e.g., PPO-Lagrangian, KCAC). ### Interpretation This legend is a critical component for interpreting a larger, unseen chart. It establishes the mapping between visual cues (color) and conceptual entities (algorithms). The presence of multiple PPO variants (`PPO`, `PPO-Lagrangian`, `RC-PPO`) indicates a study likely investigating improvements or modifications to the Proximal Policy Optimization algorithm, possibly in the context of safe or constrained reinforcement learning (suggested by `Lagrangian` and `KCAC`). The proposed method, `NSAM`, is positioned as the final entry for direct comparison against these established baselines. To fully understand the data, trends, and conclusions, this legend must be cross-referenced with the main chart it accompanies.