AGI implications for agent design in Agentic Ai - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define an AGI agent's basic decision function.

Agentic_ai

def decide_action(state):
    return [1]

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Arandom.choice(actions)

Bchoose_best_action(state)

Cprint(state)

DNone

Attempts:

3 left

2fill in blank

medium

Complete the code to update the agent's knowledge after receiving new information.

Agentic_ai

def update_knowledge(agent, info):
    agent.knowledge_base.[1](info)

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Aclear

Bremove

Cappend

Dignore

Attempts:

3 left

3fill in blank

hard

Fix the error in the agent's reward calculation function.

Agentic_ai

def calculate_reward(state, action):
    reward = state.get('value', 0) [1] action.cost
    return reward

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A-

B+

C*

D/

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps states to their rewards if reward is positive.

Agentic_ai

rewards = {state: [1] for state, action in actions.items() if [2] > 0}

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Acalculate_reward(state, action)

Bstate.value

Daction.cost

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to define an AGI agent's learning step with state, action, and reward updates.

Agentic_ai

def learning_step(agent, state, action):
    reward = [1](state, action)
    agent.memory.[2]((state, action, reward))
    agent.policy = [3](agent.memory)

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Acalculate_reward

Bappend

Cupdate_policy

Dpredict

Attempts:

3 left