Introduction to Reinforcement Learning
How agents learn by interacting with an environment and chasing rewards.
Inspired by learning material from Google
Reinforcement Learning (RL) is how AI learns to make sequences of decisions through trial and error.
The core loop
- An agent observes the state of an environment.
- It takes an action.
- It receives a reward and a new state.
- It learns a policy that maximizes long-term reward.
Exploration vs. exploitation
The agent must balance trying new actions (exploration) with using what already works (exploitation).
Why it's hard
Rewards can be delayed — an action now may only pay off much later. Credit assignment across time is a central challenge.
Famous successes
- Game-playing AI that beats world champions
- Robotics and control systems
- Optimizing data center energy use
Key takeaways
- RL learns from rewards through interaction.
- It balances exploration and exploitation.
- It excels at sequential decision-making problems.