Reinforcement Learning and Deep RL Python Theory and Projects - Action

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Information Technology (IT), Architecture, Physics, Science
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University
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Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In a real-world scenario, how can multiple agents exist in the same environment?
They need to be in different time zones.
They can coexist and interact.
They can only exist virtually.
They must be in separate rooms.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In a racing game with two cars, what does each car represent?
A rule
A single player
An agent
An environment
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is necessary for an agent to interact with its environment?
A human operator
A physical presence
A virtual reality headset
A set of actions
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In a controlled environment, what limits an agent's movement?
The agent's speed
The set of predefined rules
The agent's size
The environment's color
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How are actions encoded in a controlled environment?
Using numeric values
Through verbal commands
Via touch sensors
With color codes
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key difference between real-world and controlled environment actions?
Real-world actions are infinite.
Controlled environment actions are random.
Real-world actions are always predictable.
Controlled environment actions are limitless.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the context of the Super Mario game, what should students identify?
The game's developer
The game's soundtrack
The environment, agent, and actions
The game's release date
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