10.2.3 Reinforcement Learning

Imagine you're training a dog to do tricks. You don't tell the dog exactly how to sit; instead, you give it a treat (a reward) when it gets closer to sitting, and you don't give a treat (a punishment, or lack of reward) when it does something wrong. Over time, the dog figures out the right actions to get the treats.

Reinforcement Learning (RL) is a type of Machine Learning where an "agent" (the computer program) learns to make decisions by interacting with an "environment." It learns through trial and error, receiving rewards for good actions and penalties for bad ones. The goal of the agent is to learn a strategy (called a "policy") that maximizes its total reward over time.

Key Ideas:

Agent: The learner or decision-maker (the computer program).
Environment: The world the agent interacts with (e.g., a game, a robot's physical space).
Action: What the agent does in the environment.
State: The current situation of the environment.
Reward: A positive or negative signal the agent receives after taking an action. The agent tries to get as much positive reward as possible.

How it Works: The agent starts by trying random actions. It observes the outcome and the reward it gets. Over many trials, it starts to understand which actions lead to more rewards in different situations. It's like a continuous feedback loop.

Examples of Reinforcement Learning Use:

Training AI to play games:
- Google's DeepMind trained an AI to beat human champions at complex games like Go and chess, and even classic Atari video games, by letting it play against itself millions of times and rewarding it for winning.
Robotics:
- Teaching robots to walk, grasp objects, or perform complex movements by rewarding them for completing parts of the task correctly.
Self-driving cars (partially):
- While supervised learning is used for recognizing objects, reinforcement learning can help a self-driving car learn how to make complex driving decisions (like merging into traffic) by rewarding safe and efficient maneuvers.
Resource Management:
- Optimizing energy usage in data centers by rewarding the system for using less power while maintaining performance.

Reinforcement learning is particularly powerful for problems where there isn't a clear set of labeled examples, but where the consequences of actions can be observed and evaluated.

Bibliography:

IBM - What is reinforcement learning?: https://developer.ibm.com/learningpaths/get-started-automated-ai-for-decision-making-api/what-is-automated-ai-for-decision-making/
GeeksforGeeks - Reinforcement Learning: https://www.geeksforgeeks.org/what-is-reinforcement-learning/
Wikipedia - Reinforcement learning: https://en.wikipedia.org/wiki/Reinforcement_learning