Reinforcement learning relies on rewards and penalties to learn from "summary" of Machine Learning by Ethem Alpaydin
Reinforcement learning is a type of machine learning that relies on rewards and penalties to learn. In this framework, an agent interacts with an environment, taking actions and receiving feedback in the form of rewards or penalties. The goal of the agent is to learn a policy that maximizes its cumulative reward over time. At each time step, the agent observes the current state of the environment and selects an action to take. The environment then transitions to a new state, and the agent receives a reward or penalty based on its action. By exploring different actions and observing the consequences, the agent can learn which actions lead to higher rewards and which lead to penalties. The key idea behind reinforcement learning is the concept of reinforcement, which is used to guide the learning process. Whenever the agent takes an action that leads to a positive outcome, it receives a reward. Conversely, if the action leads to a negative outcome, the agent receives a penalty. By adjusting its behavior based on these rewards and penalties, the agent can learn to make better decisions over time. Reinforcement learning algorithms use a variety of techniques to learn from rewards and penalties. One common approach is to use a value function, which estimates the expected cumulative reward of taking a particular action in a given state. By updating this value function based on the rewards received, the agent can learn to select actions that maximize its long-term reward. Another important concept in reinforcement learning is the exploration-exploitation tradeoff. In order to learn an optimal policy, the agent must balance between exploring new actions to discover their rewards and exploiting known actions that have led to high rewards in the past. By continuously exploring and exploiting, the agent can learn to make good decisions in uncertain environments.- Reinforcement learning relies on rewards and penalties to learn a policy that maximizes cumulative reward over time. By exploring different actions, receiving feedback in the form of rewards and penalties, and adjusting its behavior based on this feedback, the agent can learn to make optimal decisions in complex environments.
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