Basic Concepts in Reinforcement Learning

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The goal is to maximize rewards over time.

Key components of RL include agents, environments, rewards, and penalties.

Key Terminology in RL

Important terms in reinforcement learning:

• Agent: The decision-maker (e.g., a robot or software).
• Environment: The system the agent interacts with.
• State: The current situation of the environment.
• Action: The decision taken by the agent.
• Reward: Feedback from the environment for an action.