What is Reinforcement Learning?

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. It tries actions, receives rewards or penalties, and gradually learns a strategy that maximizes long-term reward — like training a dog with treats.

How It Works:

1. The agent observes the current state
2. It takes an action
3. The environment returns a reward and a new state
4. The agent updates its policy to favor actions that lead to more reward
5. Repeat over many episodes

Key Concepts:

• Policy: The agent's strategy for choosing actions
• Reward signal: Feedback that defines the goal
• Exploration vs. exploitation: Trying new things vs. using what works
• Value function: Estimates future reward from a state

Where It's Used:

• Games: Superhuman play in Go, chess, and video games
• Robotics: Learning to walk or grasp objects
• Recommendations: Optimizing long-term engagement
• LLMs: RLHF aligns models with human preferences

FAQ

What is RLHF?

Reinforcement Learning from Human Feedback uses human ratings to train a reward model, then uses RL to make a language model produce more helpful, aligned responses.

Why is reinforcement learning considered hard?

Rewards can be sparse or delayed, environments can be complex, and balancing exploration with exploitation is tricky. Small design mistakes in the reward can lead to unexpected behavior.