Reinforcement Learning

Reinforcement learning is a type of machine learning that is used to teach an agent how to make decisions by trial and error. The agent learns to achieve a goal in an uncertain, potentially complex environment by interacting with the environment and receiving feedback in the form of rewards or penalties.

Agent: The learner or decision-maker that interacts with the environment.

Environment: The external system with which the agent interacts.

State: A snapshot of the environment at a given time.

Action: A decision or move made by the agent.

Reward: A scalar feedback signal that indicates how well the agent is doing.

Policy: A strategy or rule that the agent uses to make decisions.

Value Function: A function that estimates how good it is for the agent to be in a given state.

Model: A representation of the environment that the agent uses to predict the next state and reward.

Reinforcement learning is used in a wide range of applications, including:

• Game playing (e.g., AlphaGo)
• Robotics
• Autonomous driving