## 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 visually represents the distribution of goals achieved by each agent type, including the median, quartiles, and outliers.
### 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 increments 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 furthest data point within 1.5 times the IQR. Points outside the whiskers are considered outliers.
### Detailed Analysis
* **Standard agent:**
* The box extends from approximately 7 to 8 goals.
* The median is approximately 7.5 goals.
* The whiskers extend from approximately 6 to 9 goals.
* There are two outliers, one at approximately 4 goals and another at 10 goals.
* **Learning agent:**
* The box extends from approximately 8 to 10 goals.
* The median is approximately 9 goals.
* There are no visible whiskers or outliers.
### Key Observations
* The Learning agent has a higher median number of goals achieved compared to the Standard agent.
* The distribution of goals for the Learning agent appears to be more concentrated, with no visible outliers.
* The Standard agent has a wider distribution of goals and includes outliers, suggesting more variability in its performance.
### Interpretation
The box plot suggests that the Learning agent is more effective at achieving goals compared to the Standard agent. The higher median and lack of outliers indicate that the Learning agent consistently achieves a higher number of goals. The Standard agent's performance is more variable, with some instances of achieving significantly fewer or more goals than the typical range. This could be due to the Learning agent adapting and improving its performance over time, while the Standard agent's performance remains relatively constant.
