Which important class of machine learning algorithms X is defined as follows? X is concerned with how agents take actions in an environment and changing state so as to maximize some notion of cumulative reward. Explanation-based learning Inductive logic programming Deep learning Similarity-based learning Reinforcement learning
Question
Which important class of machine learning algorithms X is defined as follows? X is concerned with how agents take actions in an environment and changing state so as to maximize some notion of cumulative reward. Explanation-based learning Inductive logic programming Deep learning Similarity-based learning Reinforcement learning
Solution
The class of machine learning algorithms defined as being concerned with how agents take actions in an environment and change state so as to maximize some notion of cumulative reward is Reinforcement Learning.
Here are the steps to understand this:
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Machine Learning Algorithms: These are the algorithms that improve automatically through experience and by the use of data.
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Agents: In the context of machine learning, an agent is an algorithm or a program that interacts with an environment. The agent takes actions based on the state of the environment with the goal of achieving a task.
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Environment: This is the context or space in which the agent operates. The environment can be the real world, a simulation, a game, a database, or any other context in which the agent can take actions to change its state.
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Actions: These are the specific operations or decisions made by the agent in response to the environment's state.
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State: This is the current situation or condition of the agent in the environment.
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Cumulative Reward: This is the total reward that an agent has received over time. The goal of the agent is to maximize this cumulative reward.
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Reinforcement Learning: This is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal. The agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions based on its current state and it learns through trial and error. The agent is rewarded or penalized with a point system for each action, and it uses this system to learn which actions yield the highest reward over time.
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