Reinforcement Learning

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Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment to maximize a cumulative reward. The agent learns through trial and error, receiving feedback in the form of rewards or penalties.

Key Components:

• Agent: The learner or decision-maker.
• Environment: The external system with which the agent interacts.
• State: A description of the current situation of the environment.
• Action: A choice made by the agent.
• Reward: Feedback from the environment (positive or negative).
• Policy: The strategy the agent uses to select actions based on states.

Examples:

• Training robots to perform tasks (e.g., walking, grasping).
• Playing games (e.g., AlphaGo, self-playing video game agents).
• Optimizing resource management in complex systems.

Further topics could include Q-Learning, Deep Q-Networks (DQN), Policy Gradients, etc.