# Introduction to Reinforcement Learning Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by interacting with an environment. The agent's goal is to maximize cumulative reward over time by choosing actions that lead to desirable outcomes. ## Key Concepts - **Agent**: The learner or decision maker. - **Environment**: The system with which the agent interacts. - **State**: A representation of the current situation of the environment. - **Action**: A choice made by the agent that affects the state. - **Reward**: A scalar feedback signal received after taking an action. - **Policy**: The strategy used by the agent to decide actions based on states. - **Episode**: A sequence of states, actions, and rewards, typically ending in a terminal state. ## The RL Loop 1. The agent observes the current state of the environment. 2. The agent selects an action according to its policy. 3. The environment transitions to a new state and provides a reward. 4. The agent updates its policy based on the experience. 5. Steps 1–4 repeat until the episode ends. ## Types of Reinforcement Learning - **Model-Free RL**: The agent learns directly from experience without building a model of the environment (e.g., Q-Learning, Deep Q-Networks). - **Model-Based RL**: The agent builds a model of the environment and uses it to plan actions. - **Policy-Based Methods**: The agent learns a policy directly (e.g., Policy Gradient, Actor-Critic). - **Value-Based Methods**: The agent learns a value function to estimate future rewards (e.g., Q-Learning). ## Applications Reinforcement learning is used in: - Game playing (e.g., Chess, Go, Atari) - Robotics - Autonomous vehicles - Recommendation systems - Natural language processing ## Further Reading - Sutton, R. S., & Barto, A. G. (2018). *Reinforcement Learning: An Introduction* (2nd ed.). - [OpenAI Spinning Up in Deep RL](https://spinningup.openai.com/en/latest/) - [DeepMind RL Course](https://deepmind.com/learning-resources) --- This note provides a high-level overview. For practical examples and code, see the rest of the elsciRL documentation.