\n ## Line Chart: Goal Achievement Comparison ### Overview This image presents a line chart comparing the percentage of goals achieved by a "Learning-agent" and a "Standard-agent" as the number of goals per problem increases. The x-axis represents the number of goals per problem, ranging from 0 to 18. The y-axis represents the percentage of goals achieved, ranging from 0 to 100. ### Components/Axes * **X-axis Title:** "Goals per problem" * **Y-axis Title:** "Percentage of goals achieved" * **X-axis Scale:** Linear, from 0 to 18, with markers at integer values. * **Y-axis Scale:** Linear, from 0 to 100, with markers at 20-unit intervals. * **Legend:** Located in the top-right corner. * **Blue Line:** "Learning-agent" * **Green Line:** "Standard-agent" ### Detailed Analysis **Learning-agent (Blue Line):** The Learning-agent line starts at approximately 102% at 0 goals per problem, then decreases with a slight fluctuation. * 0 Goals: ~102% * 2 Goals: ~96% * 4 Goals: ~94% * 6 Goals: ~92% * 8 Goals: ~91% * 10 Goals: ~88% * 12 Goals: ~84% * 14 Goals: ~79% * 16 Goals: ~77% * 18 Goals: ~79% **Standard-agent (Green Line):** The Standard-agent line begins at approximately 88% at 0 goals per problem, initially dips, then exhibits a more consistent downward trend. * 0 Goals: ~88% * 2 Goals: ~89% * 4 Goals: ~82% * 6 Goals: ~79% * 8 Goals: ~77% * 10 Goals: ~69% * 12 Goals: ~66% * 14 Goals: ~64% * 16 Goals: ~61% * 18 Goals: ~59% ### Key Observations * The Learning-agent consistently outperforms the Standard-agent across all numbers of goals per problem. * Both agents show a decrease in goal achievement percentage as the number of goals per problem increases. * The Learning-agent's performance decline is less steep than that of the Standard-agent. * The Learning-agent starts with a value slightly above 100%, which is likely an artifact of the data or visualization. ### Interpretation The data suggests that the Learning-agent is more robust to increasing problem complexity (as measured by the number of goals per problem) than the Standard-agent. While both agents experience a decline in performance with more goals, the Learning-agent maintains a higher success rate. This could indicate that the Learning-agent is better at adapting to more challenging scenarios or prioritizing goals effectively. The initial value above 100% for the Learning-agent is unusual and might warrant further investigation to understand its origin. It could be a rounding error, a data anomaly, or a specific characteristic of the measurement process. The consistent downward trend for both agents suggests that there is a limit to their ability to handle increasing complexity, and that performance degrades as the number of goals increases.