Challenge - 5 Problems

🎖️

Self-Improver Master

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Test your skills under time pressure!

🧠 conceptual

intermediate

2:00remaining

What is a key characteristic of a self-improving agent?

Which of the following best describes a self-improving agent in AI?

AAn agent that randomly changes its behavior without evaluation.

BAn agent that requires manual updates from developers to improve.

CAn agent that can modify its own code or parameters to improve performance over time.

DAn agent that only follows fixed rules without any learning capability.

Attempts:

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❓ model choice

intermediate

2:00remaining

Choosing a model architecture for a self-improving agent

You want to build a self-improving agent that learns from its past decisions and adapts its strategy. Which model architecture is most suitable?

AA reinforcement learning model with policy gradient methods.

BA simple linear regression model without feedback.

CA clustering algorithm that groups data points.

DA static decision tree with fixed rules.

Attempts:

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❓ metrics

advanced

2:00remaining

Evaluating improvement in a self-improving agent

Which metric is most appropriate to measure the improvement of a self-improving agent over multiple training episodes?

AAverage cumulative reward per episode.

BMean squared error on a fixed test set.

CNumber of parameters in the model.

DTraining time per epoch.

Attempts:

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🔧 debug

advanced

2:00remaining

Debugging a self-improving agent stuck in local optimum

An agent using reinforcement learning is stuck with poor performance and does not improve despite training. What is the most likely cause?

AThe agent's model architecture is too complex.

BThe agent's learning rate is too high, causing unstable updates.

CThe reward function is too sparse, providing no feedback.

DThe agent's exploration rate is too low, causing it to exploit suboptimal actions.

Attempts:

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💻 code output

expert

2:00remaining

Output of a self-modifying agent code snippet

Consider this Python code simulating a simple self-improving agent that updates its parameter to maximize reward. What is the output after running it?

Agentic_ai

class SelfImprovingAgent:
    def __init__(self):
        self.param = 0
    def reward(self):
        return -(self.param - 5) ** 2 + 10
    def improve(self):
        best_param = self.param
        best_reward = self.reward()
        for delta in [-1, 1]:
            candidate = self.param + delta
            candidate_reward = -(candidate - 5) ** 2 + 10
            if candidate_reward > best_reward:
                best_param = candidate
                best_reward = candidate_reward
        self.param = best_param
agent = SelfImprovingAgent()
for _ in range(3):
    agent.improve()
print(agent.param)

Attempts:

2 left