Introduction to Reinforcement Learning: From Zero to AlphaGo
What is Reinforcement Learning?
Reinforcement Learning (RL) is an important branch of machine learning that studies how agents learn optimal policies in an environment through trial and error.
Core Concepts
- Agent: The subject that learns and makes decisions
- Environment: The world in which the agent operates
- State: The current situation of the environment
- Action: Operations the agent can perform
- Reward: Feedback signal from the environment about actions
Difference Between RL and Supervised Learning
| Dimension | Supervised Learning | Reinforcement Learning |
|---|---|---|
| Learning Method | Learn from labeled data | Learn from interactions |
| Feedback | Immediate correct answers | Delayed reward signals |
| Objective | Fit labels | Maximize cumulative rewards |
| Exploration | No exploration needed | Need to balance exploration vs exploitation |
Mathematical Framework: Markov Decision Process
RL problems are typically modeled as Markov Decision Processes (MDP):