Reinforcement Learning (RL)

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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

Fundamentals of Reinforcement Learning

4 topics
1.

Definition of RL

10 questions
2.

Difference between RL and supervised/unsupervised learning

10 questions
3.

Exploration vs. Exploitation

10 questions
4.

Key components: Agent, Environment, State, Action, Reward

10 questions

Markov Decision Processes (MDPs)

5 topics

Dynamic Programming

5 topics

Monte Carlo Methods

5 topics

Temporal Difference Learning

5 topics

Function Approximation

5 topics

Exploration Strategies

5 topics

Advanced Topics in RL

5 topics

Evaluation and Metrics

5 topics

Practical Implementation

5 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

Fundamentals of Reinforcement Learning
4 topics
1.
Definition of RL
10 questions
2.
Difference between RL and supervised/unsupervised learning
10 questions
3.
Exploration vs. Exploitation
10 questions
4.
Key components: Agent, Environment, State, Action, Reward
10 questions
Markov Decision Processes (MDPs)
5 topics
Dynamic Programming
5 topics
Monte Carlo Methods
5 topics
Temporal Difference Learning
5 topics
Function Approximation
5 topics
Exploration Strategies
5 topics
Advanced Topics in RL
5 topics
Evaluation and Metrics
5 topics
Practical Implementation
5 topics