Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. This guide will provide an overview of the basics of reinforcement learning.
Key Concepts
- Agent: The decision-making entity that interacts with the environment.
- Environment: The external system where the agent performs actions.
- State: The current situation or condition of the environment.
- Action: A decision made by the agent.
- Reward: A signal from the environment that indicates how well the agent performed.
Types of Reinforcement Learning
- Tabular RL: The agent has a finite set of states and actions, and the state-action value function is stored in a table.
- Model-Free RL: The agent learns from experience without having a model of the environment.
- Model-Based RL: The agent has a model of the environment and uses it to make decisions.
Learning Algorithms
- Q-Learning: An algorithm that learns the optimal policy by estimating the expected future rewards of each action.
- Sarsa: A variant of Q-Learning that considers the actual reward received after taking an action.
- Deep Q-Network (DQN): A deep learning approach that uses a neural network to approximate the Q-function.
Example
To understand reinforcement learning better, let's consider a classic example: playing a game of chess.
- Agent: The computer playing the game.
- Environment: The chessboard.
- State: The current position of the pieces on the board.
- Action: Moving a piece to a new position.
- Reward: The score of the game after each move.
The computer learns to play chess by exploring different moves and learning from the outcomes.
More Information
For a deeper understanding of reinforcement learning, we recommend checking out our comprehensive guide on Reinforcement Learning Algorithms.
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