Artificial Intelligence & Machine Learning
Reinforcement Learning
Definition
Reinforcement Learning (RL) is a type of machine learning where an "agent" learns to make decisions by performing actions in an environment to achieve some goal. The agent learns through trial and error, receiving "rewards" for good actions and "penalties" for bad ones.
Why It Matters
RL is powerful for training systems to make optimal sequences of decisions in complex, dynamic environments. It is the technology behind AI that can play complex games and control robots.
Contextual Example
DeepMind's AlphaGo was trained using reinforcement learning. The agent (the AI) played millions of games of Go against itself. It was rewarded for moves that led to winning the game and penalized for moves that led to losing, gradually learning to play at a superhuman level.
Common Misunderstandings
- RL is different from supervised learning because it does not have a labeled dataset. The agent learns from the feedback (rewards) it receives from its own actions.
- The core challenge in RL is the "credit assignment problem" - figuring out which actions in a long sequence were responsible for the final reward.