Reinforcement Learning(DL) QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

What is the term for the technique in reinforcement learning that encourages exploration by adding noise to the policy during training?

Policy evaluation

Policy optimization

Exploration strategy

Action selection

Question: 2

In reinforcement learning, what is the main purpose of using a replay buffer in Deep Q-Networks (DQN)?

To store the agent's policy

To store the history of all state-action pairs

To store experiences for training stability and sample efficiency

To store the value function

Question: 3

What is the term for the technique in reinforcement learning that reduces the learning rate as the agent gains more experience?

Exploration strategy

Learning rate annealing

Discount factor adjustment

Policy evaluation

Question: 4

Which reinforcement learning algorithm is known for its stability and ease of use in practice, often used in both continuous and discrete action spaces?

Q-learning

Trust Region Policy Optimization (TRPO)

Proximal Policy Optimization (PPO)

Monte Carlo methods

Question: 5

In reinforcement learning, what is the term for the process of estimating the expected future rewards of a state-action pair using a learned value function?

Policy optimization

Policy evaluation

Value iteration

Temporal Difference (TD) learning

Question: 6

What is the primary advantage of using policy gradient methods in reinforcement learning?

They are computationally efficient

They can handle large state spaces

They are less sensitive to hyperparameters

They are less sample-efficient

Question: 7

Which reinforcement learning algorithm uses an epsilon-greedy exploration strategy to balance exploration and exploitation?

Q-learning

Trust Region Policy Optimization (TRPO)

Proximal Policy Optimization (PPO)

Monte Carlo methods

Question: 8

What is the primary role of the critic in the Actor-Critic reinforcement learning architecture?

To select actions based on the policy

To estimate the value function

To optimize the policy

To add noise to the policy

Question: 9

In reinforcement learning, what is the term for the measure of the advantage of taking a specific action in a given state compared to the expected value?

Policy gradient

Value function

Advantage function

Q-function

Question: 10

Which reinforcement learning algorithm is specifically designed for continuous action spaces and uses an actor-critic architecture?

Q-learning

Trust Region Policy Optimization (TRPO)

Proximal Policy Optimization (PPO)

Deep Deterministic Policy Gradient (DDPG)

Question: 11

What is the primary difference between model-based and model-free reinforcement learning methods?

Model-based methods use value functions, while model-free methods use policy gradients.

Model-based methods rely on a learned model of the environment, while model-free methods do not.

Model-based methods use a deterministic policy, while model-free methods use a stochastic policy.

Model-based methods require less computational resources.

Question: 12

Which reinforcement learning algorithm is based on the idea of estimating the advantage of taking a particular action in a given state?

Q-learning

Deep Q-Network (DQN)

Policy gradient methods

Advantage Actor-Critic (A2C)

Question: 13

In reinforcement learning, what is the term for the prediction of future rewards given a specific state and action?

Value function

Policy gradient

Advantage function

Q-function

Question: 14

What is the term for the process of iteratively improving a policy through trial and error in reinforcement learning?

Policy optimization

Value iteration

Policy evaluation

Reinforcement learning

Question: 15

Which reinforcement learning algorithm uses a neural network to approximate the value function and is known for its success in game playing?

Q-learning

Deep Q-Network (DQN)

Policy gradient methods

Monte Carlo methods

Question: 16

In reinforcement learning, what is the primary goal of an agent?

To maximize rewards over time

To minimize the state space

To learn from unsupervised data

To generate training examples for a neural network

Question: 17

What is the primary difference between on-policy and off-policy methods in reinforcement learning?

On-policy methods update the policy while interacting with the environment, while off-policy methods use data from a different policy.

On-policy methods use a deterministic policy, while off-policy methods use a stochastic policy.

On-policy methods rely on value functions, while off-policy methods rely on policy gradients.

On-policy methods are faster in terms of learning convergence.

Question: 18

Which reinforcement learning algorithm is known for its ability to handle continuous action spaces and is often used in robotics?

Q-learning

Deep Q-Network (DQN)

Policy gradient methods

Trust Region Policy Optimization (TRPO)

Question: 19

What is the primary limitation of using a high discount factor in reinforcement learning?

It leads to overly optimistic estimates of future rewards.

It causes the agent to prioritize immediate rewards over long-term benefits.

It increases the complexity of the policy.

It slows down the learning process.

Question: 20

In reinforcement learning, what does the term "exploration vs. exploitation" refer to?

Balancing the trade-off between learning and taking the best-known action

Determining the agent's policy

Calculating the cumulative reward

Selecting the action with the highest probability

Question: 21

What is the term for the numerical factor used to discount future rewards in reinforcement learning?

Exploration rate

Discount factor

Learning rate

Policy gradient

Question: 22

Which reinforcement learning approach combines elements of both value-based and policy-based methods by using a value function and a policy?

Model-free learning

Q-learning

Actor-Critic

Monte Carlo methods

Question: 23

What is the primary difference between on-policy and off-policy reinforcement learning methods?

On-policy methods use value functions, while off-policy methods use policy gradients.

On-policy methods learn from the agent's own policy, while off-policy methods can learn from different policies.

On-policy methods require a fixed exploration strategy, while off-policy methods do not.

On-policy methods use a deterministic policy, while off-policy methods use a stochastic policy.

Question: 24

In reinforcement learning, what is the term for the measure of the long-term expected rewards for an agent following a policy?

Value function

Policy gradient

Advantage function

Q-function

Question: 25

Which reinforcement learning algorithm estimates the value of being in a particular state and following a particular policy?

Policy gradient methods

Q-learning

Actor-Critic

Temporal Difference (TD) learning

Question: 26

What is the term for the mapping from states to actions in reinforcement learning?

Value function

Policy

Q-function

Reward function

Question: 27

Which reinforcement learning paradigm involves learning from trial and error by interacting with the environment?

Model-based learning

Value iteration

Model-free learning

Q-learning

Question: 28

What is the role of a reward signal in reinforcement learning?

To determine the agent's policy

To punish the agent for incorrect actions

To specify the agent's exploration strategy

To provide feedback to the agent about its actions

Question: 29

In reinforcement learning, what is the environment?

The set of possible actions

The agent's internal memory

The world or system the agent interacts with

The reward function

Question: 30

What is the term used to describe the process of selecting actions to maximize expected rewards in reinforcement learning?

Supervised learning

Unsupervised learning

Policy optimization

Feature extraction

Reinforcement Learning(DL) QUIZ (MCQ QUESTIONS AND ANSWERS)

TIME:20:00

Time:20:00

What is the term for the technique in reinforcement learning that encourages exploration by adding noise to the policy during training?

In reinforcement learning, what is the main purpose of using a replay buffer in Deep Q-Networks (DQN)?

What is the term for the technique in reinforcement learning that reduces the learning rate as the agent gains more experience?

Which reinforcement learning algorithm is known for its stability and ease of use in practice, often used in both continuous and discrete action spaces?

In reinforcement learning, what is the term for the process of estimating the expected future rewards of a state-action pair using a learned value function?

What is the primary advantage of using policy gradient methods in reinforcement learning?

Which reinforcement learning algorithm uses an epsilon-greedy exploration strategy to balance exploration and exploitation?

What is the primary role of the critic in the Actor-Critic reinforcement learning architecture?

In reinforcement learning, what is the term for the measure of the advantage of taking a specific action in a given state compared to the expected value?

Which reinforcement learning algorithm is specifically designed for continuous action spaces and uses an actor-critic architecture?

What is the primary difference between model-based and model-free reinforcement learning methods?

Which reinforcement learning algorithm is based on the idea of estimating the advantage of taking a particular action in a given state?

In reinforcement learning, what is the term for the prediction of future rewards given a specific state and action?

What is the term for the process of iteratively improving a policy through trial and error in reinforcement learning?

Which reinforcement learning algorithm uses a neural network to approximate the value function and is known for its success in game playing?

In reinforcement learning, what is the primary goal of an agent?

What is the primary difference between on-policy and off-policy methods in reinforcement learning?

Which reinforcement learning algorithm is known for its ability to handle continuous action spaces and is often used in robotics?

What is the primary limitation of using a high discount factor in reinforcement learning?

In reinforcement learning, what does the term "exploration vs. exploitation" refer to?

What is the term for the numerical factor used to discount future rewards in reinforcement learning?

Which reinforcement learning approach combines elements of both value-based and policy-based methods by using a value function and a policy?

What is the primary difference between on-policy and off-policy reinforcement learning methods?

In reinforcement learning, what is the term for the measure of the long-term expected rewards for an agent following a policy?

Which reinforcement learning algorithm estimates the value of being in a particular state and following a particular policy?

What is the term for the mapping from states to actions in reinforcement learning?

Which reinforcement learning paradigm involves learning from trial and error by interacting with the environment?

What is the role of a reward signal in reinforcement learning?

In reinforcement learning, what is the environment?

What is the term used to describe the process of selecting actions to maximize expected rewards in reinforcement learning?