COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Supervised learning algorithm
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Unsupervised learning algorithm
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Semi-supervised learning algorithm
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Reinforcement learning algorithm
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Detailed explanation-1: -Q-Learning is a Reinforcement learning policy that will find the next best action, given a current state. It chooses this action at random and aims to maximize the reward.
Detailed explanation-2: -Q-learning is a model-free reinforcement learning algorithm. Q-learning is a values-based learning algorithm. Value based algorithms updates the value function based on an equation(particularly Bellman equation).
Detailed explanation-3: -Reinforcement learning is supervised learning on optimized data.
Detailed explanation-4: -Markov Decision Process is a Reinforcement Learning algorithm that gives us a way to formalize sequential decision making.
Detailed explanation-5: -The Q-learning algorithm uses a Q-table of State-Action Values (also called Q-values). This Q-table has a row for each state and a column for each action. Each cell contains the estimated Q-value for the corresponding state-action pair. We start by initializing all the Q-values to zero.