Reinforcement Learning, or RL, is a way to train an agent by letting it interact with an environment and learn from rewards. Instead of learning from a fixed table of labeled examples, the agent learns by trying actions and seeing what happens.
Why it matters
RL is useful when decisions affect future states: robotics, game AI, recommendation strategies, resource allocation, operations research, pricing, and some alignment workflows. It teaches the important idea that short-term rewards and long-term outcomes can conflict.
Key terms
- Agent: the learner or decision-maker.
- Environment: the world the agent interacts with.
- State: the current situation observed by the agent.
- Action: a choice the agent can make.
- Reward: feedback signal that tells the agent how good an outcome was.
- Policy: the agent's strategy for choosing actions.
- Episode: one full rollout from start to finish.
- Discount factor: how much the agent values future rewards compared with immediate rewards.
Exploration vs exploitation
An RL agent must explore new actions to discover better strategies, but it must also exploit actions that already seem good. Too much exploration wastes time; too little exploration can trap the agent in a weak strategy.
Visual explanation suggestion
Show an agent moving on a grid with rewards and penalties. Let learners change exploration rate and discount factor, then watch the learned path become safer or more reward-focused.
Common mistakes
- Designing a reward that accidentally encourages the wrong behavior.
- Evaluating only one lucky episode instead of many episodes.
- Ignoring safety constraints while the agent explores.
- Using RL when supervised learning or optimization would solve the problem more simply.
Interview-style questions
- What is the difference between supervised learning and reinforcement learning?
- Explain agent, environment, state, action, and reward with one example.
- What is the exploration-exploitation tradeoff?
- Why is reward design difficult in reinforcement learning?
Related lessons
- Gradient Descent
- Q-Learning & Deep RL
- RLHF - Human Feedback Training
- AI Ethics & Bias
Related project/template CTA
Use the visual course path to understand RL basics before applying monitoring and safety checks from the MLOps Starter Kit.