Reinforcement Learning

Imagine training a dog: you don't show it thousands of labeled examples of "sit." Instead, you give treats when it sits and say "no" when it doesn't. Over time, the dog learns what actions lead to rewards.

Reinforcement Learning (RL) works the same way. An agent learns by interacting with an environment, receiving rewards for good actions and penalties for bad ones.

The Reinforcement Learning Framework

Key Concepts

A Simple Example: Grid World

Exploration vs Exploitation

A key challenge in RL: should the agent try new things or stick with what works?

RL vs Supervised Learning

Real-World Applications

The Three Types Compared

Key Takeaways

• Reinforcement Learning learns through interaction and feedback
• An agent takes actions in an environment and receives rewards
• The goal is to learn a policy that maximizes cumulative reward
• Key challenge: balancing exploration (trying new things) vs exploitation (using what works)
• Used for games, robotics, autonomous vehicles, and sequential decision-making
• Different from supervised (needs labels) and unsupervised (finds patterns)

In the next module, we'll set up our ML toolkit with NumPy, Pandas, and scikit-learn!