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...
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