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Reinforcement Learning (RL)

Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards. The agent takes actions, receives feedback in the form of rewards or penalties, and updates its strategy accordingly. RL is widely used in robotics, game playing, autonomous systems, and recommendation systems.

Key Components of RL:

1. Agent: The entity that makes decisions.

2. Environment: The system the agent interacts with.

3. State ($S$): The current situation of the agent in the environment.

4. Actions ($A$): The possible moves the agent can make.

5. Reward ($R$): Feedback received after taking an action.

6. Policy ($\pi$): The strategy for choosing actions.

7. Value Function ($V$): The expected long-term reward of a state.

8. Q-Function ($Q$): The expected reward of taking a specific action in a state.

Difference Between Reinforcement Learning and Supervised Learning

Feature	Reinforcement Learning (RL)	Supervised Learning
Data Dependency	Learns from interactions with an environment.	Learns from labeled datasets.
Feedback Type	Reward signal after each action.	Explicit correct answers (labels).
Objective	Maximizing long-term cumulative reward.	Minimizing prediction error.
Exploration vs. Exploitation	Balances trying new actions vs. using known best actions.	No concept of exploration, directly learns from data.
Examples	AlphaGo, self-driving cars, robotics.	Image classification, spam detection, sentiment analysis.

Unlike supervised learning, where the model is trained on fixed data, RL adapts dynamically, making it suitable for decision-making in uncertain environments.