## Box Plot: Number of Goals Achieved by Standard and Learning Agents ### Overview The image is a box plot comparing the number of goals achieved by a "Standard agent" and a "Learning agent." The plot displays the distribution of goals achieved by each agent, showing the median, quartiles, and range. ### Components/Axes * **X-axis:** Categorical axis with two categories: "Standard agent" and "Learning agent." * **Y-axis:** Numerical axis labeled "Number of goals," ranging from 2 to 20, with tick marks at intervals of 2. * **Box Plots:** Each box plot represents the distribution of goals for each agent type. The box represents the interquartile range (IQR), the line inside the box represents the median, and the whiskers extend to the minimum and maximum values within 1.5 times the IQR. ### Detailed Analysis * **Standard agent:** * Minimum number of goals: Approximately 4 * First quartile (Q1): Approximately 6 * Median: Approximately 7 * Third quartile (Q3): Approximately 8 * Maximum number of goals: Approximately 10 * **Learning agent:** * Minimum number of goals: Approximately 6 * First quartile (Q1): Approximately 6 * Median: Approximately 8 * Third quartile (Q3): Approximately 10 * Maximum number of goals: Approximately 10 ### Key Observations * The Learning agent has a higher median number of goals achieved compared to the Standard agent. * The range of goals achieved by the Learning agent is smaller than that of the Standard agent. * The interquartile range (IQR) for the Learning agent is higher than the Standard agent. ### Interpretation The box plot suggests that the Learning agent tends to achieve a higher number of goals compared to the Standard agent. The smaller range for the Learning agent indicates more consistent performance. The higher IQR for the Learning agent suggests that the middle 50% of the data is more spread out than the Standard agent. Overall, the Learning agent appears to be more effective in achieving goals.

## Chart Type: Box Plot of Number of Goals for Different Agent Types ### Overview This image displays a box plot comparing the distribution of "Number of goals" achieved by two distinct agent types: a "Standard agent" and a "Learning agent". The chart uses a vertical axis for the number of goals and a horizontal axis to categorize the agent types. ### Components/Axes * **Y-axis (Vertical Axis)**: * **Title**: "Number of goals" * **Scale**: Ranges from 2 to 20. * **Tick Markers**: Labeled at intervals of 2 (2, 4, 6, 8, 10, 12, 14, 16, 18, 20). * **X-axis (Horizontal Axis)**: * **Categories**: Two distinct categories are presented: * "Standard agent" (positioned on the left) * "Learning agent" (positioned on the right) * **Legend**: There is no explicit legend. The X-axis labels directly identify the data series represented by each box plot. ### Detailed Analysis The chart presents two box plots, each representing the distribution of "Number of goals" for its respective agent type. Each box plot consists of a box, an internal line (median), and whiskers extending from the box. 1. **Standard agent (left box plot)**: * **Visual Trend/Distribution**: This box plot shows a distribution of goals ranging from approximately 4 to 10. The central tendency (median) is lower than the learning agent, and its interquartile range is relatively narrow. * **Median (orange line inside the box)**: Approximately 7 goals. * **Interquartile Range (IQR - the box itself)**: * **25th Percentile (Q1 - bottom of the box)**: Approximately 6 goals. * **75th Percentile (Q3 - top of the box)**: Approximately 8 goals. * **Whiskers**: * **Lower Whisker (bottom line)**: Extends down to approximately 4 goals. * **Upper Whisker (top line)**: Extends up to approximately 10 goals. * **Range**: The data for the standard agent spans from approximately 4 goals to 10 goals. 2. **Learning agent (right box plot)**: * **Visual Trend/Distribution**: This box plot shows a distribution of goals ranging from approximately 6 to 10. It has a higher median and a more compressed upper half of its distribution compared to the standard agent. * **Median (orange line inside the box)**: Exactly 10 goals. * **Interquartile Range (IQR - the box itself)**: * **25th Percentile (Q1 - bottom of the box)**: Exactly 6 goals. * **75th Percentile (Q3 - top of the box)**: Exactly 10 goals. * **Whiskers**: * **Lower Whisker (bottom line)**: Extends down to exactly 6 goals. This indicates that the minimum value is at the 25th percentile. * **Upper Whisker (top line)**: Extends up to exactly 10 goals. This indicates that the maximum value is at the 75th percentile, which is also the median. * **Range**: The data for the learning agent spans from approximately 6 goals to 10 goals. ### Key Observations * The "Learning agent" generally achieves a higher number of goals than the "Standard agent". * The median number of goals for the "Learning agent" (10 goals) is significantly higher than that for the "Standard agent" (approximately 7 goals). * The "Learning agent" shows a tighter distribution in its upper half, with its median, 75th percentile, and maximum value all converging at 10 goals. This suggests that at least 50% of the "Learning agent" data points are at 10 goals. * The "Standard agent" has a wider overall range (4 to 10 goals) compared to the "Learning agent" (6 to 10 goals), particularly at the lower end. * Both agents share a similar lower quartile (Q1) at approximately 6 goals, indicating that at least 25% of the data for both agents falls below or at this value. ### Interpretation The box plot clearly demonstrates a performance advantage for the "Learning agent" over the "Standard agent" in terms of the "Number of goals" achieved. The "Learning agent" consistently reaches a higher number of goals, with its median performance matching the maximum performance of the "Standard agent". Specifically, the "Learning agent" achieves 10 goals in at least 50% of its trials (from the median to Q3, and also the maximum), while the "Standard agent" only reaches 10 goals at its absolute best performance (upper whisker). The "Standard agent" also exhibits a wider spread of outcomes, including instances where it achieves as few as 4 goals, which is lower than any outcome observed for the "Learning agent". This data suggests that the "Learning agent" is not only more effective at achieving goals on average but also more consistent in its higher performance, with a significant portion of its outcomes clustered at the maximum observed goal count. The "Standard agent," while capable of reaching the same maximum, has a lower typical performance and a greater variability in its results. This could imply that the learning mechanism provides a more robust and optimized strategy for goal attainment.

\n ## Box Plot: Number of Goals Scored by Agent Type ### Overview The image presents a box plot comparing the number of goals scored by two types of agents: a "Standard agent" and a "Learning agent". The plot displays the distribution of goal counts for each agent type, showing the median, quartiles, and potential outliers. ### Components/Axes * **X-axis:** Agent Type - with categories "Standard agent" and "Learning agent". * **Y-axis:** Number of goals - ranging from 2 to 20, with increments of 2. * **Box Plots:** Two box plots, one for each agent type. Each box plot includes: * A box representing the interquartile range (IQR). * A horizontal line inside the box representing the median. * "Whiskers" extending from the box, indicating the range of the data (excluding outliers). * Potential outlier points beyond the whiskers. ### Detailed Analysis **Standard Agent:** The box plot for the Standard agent shows a median of approximately 7 goals. The box extends from approximately 5.5 to 8 goals, indicating the IQR. The whiskers extend down to approximately 4 goals and up to approximately 9 goals. There are no visible outliers. **Learning Agent:** The box plot for the Learning agent shows a median of approximately 9.5 goals. The box extends from approximately 7 to 10 goals, indicating the IQR. The whiskers extend down to approximately 6.5 goals and up to approximately 10 goals. There are no visible outliers. ### Key Observations * The Learning agent appears to score more goals on average than the Standard agent, as indicated by the higher median. * The distribution of goal counts for the Learning agent is more concentrated, as indicated by the smaller IQR. * Both agents have similar ranges of goal counts, as indicated by the similar whisker lengths. * There are no apparent outliers in either dataset. ### Interpretation The data suggests that the Learning agent is more effective at scoring goals than the Standard agent. The higher median and more concentrated distribution of goal counts for the Learning agent indicate that it consistently scores more goals. This could be due to the Learning agent's ability to adapt and improve its performance over time, while the Standard agent's performance remains relatively constant. The lack of outliers suggests that both agents perform consistently within a certain range, without any extreme deviations. This data could be used to evaluate the effectiveness of the learning algorithm used by the Learning agent and to identify areas for improvement in the Standard agent's strategy.

## Box Plot: Number of Goals by Agent Type ### Overview The image is a vertical box plot comparing the distribution of the "Number of goals" achieved by two distinct agent types: a "Standard agent" and a "Learning agent." The chart is presented on a white background with a simple, clean aesthetic. ### Components/Axes * **Chart Type:** Box-and-whisker plot (box plot). * **Y-Axis (Vertical):** * **Label:** "Number of goals" * **Scale:** Linear, ranging from 2 to 20. * **Major Tick Marks:** At intervals of 2 (2, 4, 6, 8, 10, 12, 14, 16, 18, 20). * **X-Axis (Horizontal):** * **Categories:** Two discrete categories are labeled below their respective box plots. * **Left Category:** "Standard agent" * **Right Category:** "Learning agent" * **Plot Elements (Legend Implied by Color/Style):** * **Orange Horizontal Line:** Represents the **median** (50th percentile) of the data. * **Black Box:** Represents the **Interquartile Range (IQR)**, spanning from the 25th percentile (Q1, bottom of box) to the 75th percentile (Q3, top of box). * **Black "Whiskers":** Extend from the box to the minimum and maximum data points within 1.5 * IQR from Q1 and Q3, respectively. Points beyond this range would typically be plotted as outliers (none are visible here). ### Detailed Analysis **1. Standard Agent (Left Box Plot):** * **Median (Orange Line):** Approximately **7**. * **Interquartile Range (Black Box):** * **Q1 (25th Percentile, Bottom of Box):** Approximately **6**. * **Q3 (75th Percentile, Top of Box):** Approximately **8**. * **IQR Height:** ~2 units. * **Whiskers:** * **Minimum (Lower Whisker End):** Approximately **4**. * **Maximum (Upper Whisker End):** Approximately **10**. * **Visual Trend:** The distribution is centered around 7 goals, with the middle 50% of data falling between 6 and 8 goals. The full range of typical data (excluding outliers) spans from 4 to 10 goals. **2. Learning Agent (Right Box Plot):** * **Median (Orange Line):** Approximately **10**. * **Interquartile Range (Black Box):** * **Q1 (25th Percentile, Bottom of Box):** Approximately **6**. * **Q3 (75th Percentile, Top of Box):** Approximately **10**. * **IQR Height:** ~4 units. * **Whiskers:** * **Minimum (Lower Whisker End):** Approximately **6**. * **Maximum (Upper Whisker End):** The upper whisker is not visible. This indicates that the maximum value within the 1.5*IQR range is equal to Q3 (10), or that the data is capped at 10 for the upper half of the distribution. * **Visual Trend:** The distribution is skewed. The median is at the top of the box (Q3), indicating that at least 50% of the data points are at or below 10 goals, and at least 25% are at or above 10 goals. The lower 50% of the data is spread between 6 and 10 goals. ### Key Observations 1. **Higher Central Tendency for Learning Agent:** The median number of goals for the Learning agent (10) is significantly higher than that of the Standard agent (7). 2. **Different Distribution Shapes:** * The Standard agent's distribution is relatively symmetric around its median. * The Learning agent's distribution is negatively skewed (left-skewed), with the median coinciding with the third quartile (Q3). This suggests a concentration of higher values. 3. **Overlap in Lower Ranges:** Both agents have a similar lower quartile (Q1 ≈ 6), indicating that the worst 25% of performances for both agents are comparable. 4. **Greater Upper-Range Performance for Learning Agent:** The Learning agent's interquartile range extends to 10, which is the maximum value for the Standard agent's typical range. This shows the Learning agent consistently achieves higher goal counts in its upper 75% of results. 5. **No Visible Outliers:** Neither plot shows individual points beyond the whiskers, suggesting all data points fall within the calculated expected range. ### Interpretation The data suggests a clear performance difference between the two agent types in the context of achieving goals. * **The Learning agent demonstrates superior median performance,** achieving approximately 43% more goals at the median level (10 vs. 7) compared to the Standard agent. * **The Learning agent shows a "ceiling effect" or high consistency at the top end.** The fact that its median equals its 75th percentile (Q3) and the upper whisker is absent implies that a substantial portion (at least 25%) of its runs achieve the maximum observed value of 10 goals, and no run in the typical range exceeds this. This could indicate a performance cap in the experimental setup or that the agent reliably reaches a high-performance plateau. * **The Standard agent has a more predictable, symmetric spread** of outcomes, centered on a lower goal count. * **The similar lower bounds (Q1 and minimum)** suggest that both agents have a comparable "worst-case" scenario, failing to achieve more than 4-6 goals in their poorest performances. **Conclusion:** The "Learning agent" is not only more effective on average (higher median) but also more reliably achieves higher goal counts, with its lower 25% of results matching the Standard agent's lower 25%. The Standard agent's performance is more variable around a lower mean. This visualization strongly supports the claim that the Learning agent architecture improves goal-achievement outcomes over the Standard agent in this measured context.

## Box Plot: Number of Goals by Agent Type ### Overview The image displays a comparative box plot analyzing the distribution of goals scored by two types of agents: "Standard agent" and "Learning agent." The y-axis represents the "Number of goals" (ranging from 2 to 20), while the x-axis categorizes the agents. The plot uses black boxes with orange median lines to represent data distributions. ### Components/Axes - **Y-axis**: "Number of goals" (scale: 2 to 20, increments of 2). - **X-axis**: Two categories: "Standard agent" (left) and "Learning agent" (right). - **Legend**: No explicit legend is present. Colors are directly embedded in the plot (black boxes, orange median lines). - **Whiskers**: Extend from the boxes to represent data range (minimum to maximum values excluding outliers). ### Detailed Analysis 1. **Standard Agent**: - **Median**: Approximately 7 (orange line within the box). - **Interquartile Range (IQR)**: Box spans from 6 (Q1) to 8 (Q3). - **Whiskers**: Extend from 4 (minimum) to 10 (maximum). - **Outliers**: None visible. 2. **Learning Agent**: - **Median**: Approximately 8 (orange line within the box). - **Interquartile Range (IQR)**: Box spans from 6 (Q1) to 10 (Q3). - **Whiskers**: Extend from 6 (minimum) to 10 (maximum). - **Outliers**: None visible. ### Key Observations - The **Learning agent** exhibits a higher median goal count (8 vs. 7) and a larger IQR (4 vs. 2), indicating greater variability in performance. - The **Standard agent** shows a tighter distribution, with data concentrated between 6 and 8 goals. - Both agents share identical whisker ranges (4–10 for Standard; 6–10 for Learning), suggesting similar extreme values but differing central tendencies. ### Interpretation The data suggests that the **Learning agent** outperforms the Standard agent on average, achieving a higher median goal count. However, the Learning agent’s performance is less consistent, as evidenced by its broader IQR. The Standard agent, while less effective overall, demonstrates more predictable outcomes. The absence of outliers in both groups implies stable performance under the tested conditions. This could indicate that the Learning agent’s adaptive mechanisms improve average results but introduce variability, whereas the Standard agent’s fixed strategy prioritizes consistency over optimization.