
ML-Terms used in Reinforcement Learning
Authored by KarunaiMuthu SriRam
Computers
University
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15 questions
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1.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
In Reinforcement Learning, what is the definition of an "episode"?
The number of steps the agent takes in the environment
A sequence of actions taken by the agent
A complete interaction cycle from the initial state to the terminal state
The set of states the agent can be in during the learning process
2.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What does the term "reward function" represent in Reinforcement Learning?
A mathematical function that estimates the optimal policy
The measure of how good the agent's policy is
The set of actions the agent can choose from
The function that calculates the immediate feedback for each action
3.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What is the purpose of the "value function" in Reinforcement Learning?
To compute the cumulative rewards for each action
To measure the uncertainty of the agent's actions
To estimate the expected long-term return from a given state
To calculate the difference between the expected and actual rewards
4.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
In the context of Reinforcement Learning, what is "exploration"?
The process of learning from a teacher's demonstrations
The strategy of selecting known actions with the highest rewards
The process of interacting with the environment and trying new actions
The measure of the agent's performance compared to a baseline
5.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What does the term "exploitation" refer to in Reinforcement Learning?
The process of maximizing the cumulative rewards
The strategy of selecting actions with the highest estimated value
The measurement of how well the agent is performing
The process of updating the agent's policy
6.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
In the context of Reinforcement Learning, what does "discount factor" represent?
The difference between the expected and actual rewards
The probability of taking a certain action in the environment
The measure of how good the agent's decisions are
The importance given to future rewards compared to immediate rewards
7.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What is the "action space" in Reinforcement Learning?
The set of all possible actions an agent can take in the environment
The sequence of states the agent traverses during learning
The process of adjusting the agent's policy to improve performance
The set of training data samples used for learning
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