Reinforcement Learning (RL)

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About the Quiz

Quiz will ask 20 randomly selected questions with allotted time of . You can take the quiz more than once. Once you submit the quiz, you can review how you have done, the correct the answers for each questions and the explanation for the correct the answer.

Quiz Topics

10 Modules

Practical Implementation

5 topics
1.

Case studies and real-world applications

10 questions
2.

Debugging and troubleshooting RL algorithms

10 questions
3.

Hyperparameter tuning

10 questions
4.

Popular RL libraries (e.g., OpenAI Gym, Stable Baselines)

10 questions
5.

Setting up environments for RL

10 questions

Evaluation and Metrics

5 topics

Advanced Topics in RL

5 topics

Exploration Strategies

5 topics

Function Approximation

5 topics

Temporal Difference Learning

5 topics

Monte Carlo Methods

5 topics

Dynamic Programming

5 topics

Markov Decision Processes (MDPs)

5 topics

Fundamentals of Reinforcement Learning

4 topics
Sample questions

What are the main components of a Reinforcement Learning (RL) problem?

Agent

Environment

Reward

State

All of the above

In the context of RL, what does the term 'policy' refer to?

A strategy used by the agent to decide actions

The environment's response to the agent's actions

The cumulative reward received by the agent

The state transition probabilities

Which of the following best describes the exploration-exploitation trade-off in RL?

Choosing between known rewards and discovering new rewards

Balancing the training time and model accuracy

Deciding between deterministic and stochastic policies

Managing the complexity of the state space

What is the Bellman equation used for in Reinforcement Learning?

To compute the optimal policy

To calculate the value function

To update the Q-values

To define the reward structure

Which of the following algorithms is NOT typically associated with Reinforcement Learning?

Q-Learning

SARSA

Gradient Descent

Deep Q-Networks (DQN)

Quiz Topics

10 Modules

Practical Implementation

5 topics
1.

Case studies and real-world applications

10 questions
2.

Debugging and troubleshooting RL algorithms

10 questions
3.

Hyperparameter tuning

10 questions
4.

Popular RL libraries (e.g., OpenAI Gym, Stable Baselines)

10 questions
5.

Setting up environments for RL

10 questions

Evaluation and Metrics

5 topics

Advanced Topics in RL

5 topics

Exploration Strategies

5 topics

Function Approximation

5 topics

Temporal Difference Learning

5 topics

Monte Carlo Methods

5 topics

Dynamic Programming

5 topics

Markov Decision Processes (MDPs)

5 topics

Fundamentals of Reinforcement Learning

4 topics