Reinforcement Learning Fundamentals

Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. This tutorial will cover the basics of reinforcement learning, including key concepts and algorithms.

Key Concepts

• Agent: The entity that learns from the environment. It can be a software program or a robot.
• Environment: The system where the agent operates. It provides the agent with information about the current state and allows it to take actions.
• State: The current situation or configuration of the environment.
• Action: A step taken by the agent to change the state of the environment.
• Reward: A numerical value indicating how good or bad an action was.
• Policy: A strategy or set of rules that the agent uses to determine the next action.

Algorithms

• Q-Learning: A value-based algorithm that learns a value function for each state-action pair.
• Deep Q-Network (DQN): A deep learning algorithm that extends Q-Learning to handle high-dimensional input spaces.
• Policy Gradient: An algorithm that directly learns a policy rather than a value function.

Getting Started

To get started with reinforcement learning, you can visit our Reinforcement Learning with Python tutorial. It provides a step-by-step guide to building your first RL agent.

Reinforcement Learning

Conclusion

Reinforcement learning is a powerful tool for building intelligent agents that can learn to make decisions in complex environments. By understanding the key concepts and algorithms, you can start building your own RL agents today.