
Exploring Reinforcement Learning Concepts
Authored by sherinshibi charles
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15 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a Markov Decision Process (MDP)?
A Markov Decision Process (MDP) is a type of neural network.
A Markov Decision Process (MDP) is a method for sorting data in databases.
A Markov Decision Process (MDP) is a statistical model for predicting weather patterns.
A Markov Decision Process (MDP) is a framework for modeling decision-making with states, actions, transition probabilities, and rewards.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Define the Multi-Armed Bandit Problem.
A strategy for maximizing profits in stock trading.
A method for solving linear equations.
A game where players compete to collect the most coins.
The Multi-Armed Bandit Problem is a decision-making problem where a gambler must choose between multiple options (arms) to maximize rewards, balancing exploration and exploitation.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the epsilon in epsilon-greedy represent?
Epsilon indicates the maximum reward achievable.
Epsilon is the fixed value for the learning rate.
Epsilon represents the total number of actions taken.
Epsilon represents the probability of exploration in the epsilon-greedy algorithm.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Explain the concept of exploration vs. exploitation.
Exploration is only about maximizing rewards.
Exploitation involves taking risks without prior knowledge.
Exploration and exploitation are the same process.
Exploration is the act of seeking new information or options, while exploitation is the act of using known information or options to maximize rewards.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of the reward function in MDPs?
The reward function only tracks the agent's performance over time.
The reward function provides feedback to guide the agent's decision-making process.
The reward function determines the optimal policy directly.
The reward function is used to initialize the MDP.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does the UCB (Upper Confidence Bound) algorithm work?
The UCB algorithm only focuses on exploitation without considering exploration.
The UCB algorithm randomly selects actions without any strategy.
The UCB algorithm uses a fixed set of actions without updating based on performance.
The UCB algorithm selects actions by maximizing the upper confidence bound, balancing exploration and exploitation.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are the key components of a Markov Decision Process?
States, Actions, Probability Distribution
States, Actions, Policy
States, Actions, Value Function
States, Actions, Transition Model, Reward Function, Discount Factor
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