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 tutorial will guide you through the basics of RL, including key concepts, algorithms, and practical applications.
Key Concepts
- Agent: The decision-making entity in the environment.
- Environment: The context in which the agent operates.
- State: The current situation or condition of the environment.
- Action: The decision made by the agent.
- Reward: The feedback received by the agent after taking an action.
Algorithms
- Q-Learning: A value-based algorithm that learns the optimal action-value function.
- Policy Gradient: An algorithm that learns the optimal policy directly.
- Deep Q-Network (DQN): A combination of Q-Learning and deep learning for complex environments.
Practical Applications
- Robotics: Teaching robots to navigate and manipulate objects.
- Games: Developing AI agents for playing games like chess, Go, and poker.
- Finance: Optimizing investment strategies and risk management.
- Healthcare: Personalized medicine and disease prediction.
Further Reading
For more in-depth information on Reinforcement Learning, check out our comprehensive guide on Reinforcement Learning Fundamentals.
Reinforcement Learning