What are the basic concepts of reinforcement learning?

What are the basic concepts of reinforcement learning?

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.

Can you explain the difference between exploration and exploitation?

Sure! Exploration involves trying new actions to discover their effects, while exploitation means choosing the best-known action to maximize reward based on past experiences.

What is a reward function?

A reward function defines the goal of the reinforcement learning problem by providing feedback to the agent based on its actions, guiding it towards desired outcomes.

How does Q-learning work?

Q-learning is a model-free RL algorithm that learns the value of actions in states, allowing the agent to choose actions that maximize future rewards.

What are some common applications of reinforcement learning?

Common applications include robotics, game playing, recommendation systems, and autonomous vehicles.