1. Fundamentals of reinforcement learning
Welcome, everyone! My name is Fouad Trad, and it's my pleasure to guide us through the intriguing and dynamic world of reinforcement learning, an essential branch of machine learning. Let's get to it!
2. Reinforcement learning
Reinforcement Learning, or RL for short, is a unique facet of machine learning
where an agent learns to make decisions through trial and error.
Unlike other forms of machine learning, RL involves an agent
3. Reinforcement learning
that observes
4. Reinforcement learning
and acts within an environment
5. Reinforcement learning
receiving rewards for good decisions
and penalties for bad ones.
The agent’s goal is to devise a strategy that maximizes positive feedback over time.
6. RL as training a pet
We can think of RL as training a pet. Just as we would reward our pet for following a command or performing a trick correctly, in RL, an agent receives rewards for making the correct decisions in its environment. The process is iterative and based on trial and error, much like how a pet learns from repeated training sessions.
7. RL vs. other ML types
RL differs significantly from other types of machine learning, such as supervised and unsupervised learning.
In supervised learning, models are trained using labeled data, learning to predict outcomes based on examples. It is suitable for solving problems like classification and regression.
8. RL vs. other ML types
Unsupervised learning, on the other hand, involves learning to identify patterns or structures from unlabeled data. It is suitable for solving problems like clustering or association analysis.
9. RL vs. other ML types
RL, distinct from both, does not use any training data, and learns through trial and error to perform actions that maximize the reward, making it ideal for decision-making tasks.
10. When to use RL?
In particular, RL is well-suited for scenarios that require training a model to make sequential decisions
where each decision influences future observations.
In this setting, the agent learns through rewards and penalties.
These guide it towards developing more effective strategies without any kind of direct supervision.
11. Appropriate for RL: playing video games
An appropriate example for RL is playing video games,
where the player needs to make sequential decisions such as jumping over obstacles or avoiding enemies.
The player learns and improves by trial and error, receiving points for successful actions and losing lives for mistakes. The goal is to maximize the score by learning the best strategies to overcome the game's challenges.
12. Inappropriate for RL: in-game object recognition
Conversely, RL is unsuitable for tasks such as in-game object recognition, where the objective is to identify and classify elements like characters or items in a video frame.
This task does not involve sequential decision-making
or interaction with an environment. Instead, supervised learning, which employs labeled data to train models in recognizing and categorizing objects, proves more effective for this purpose.
13. RL applications
Beyond its well-known use in gaming, RL has a myriad of applications across various sectors. In robotics, RL is pivotal for teaching robots tasks through trial and error,
like walking
or object manipulation.
14. RL applications
The finance industry leverages RL for
optimizing trading and investment strategies
to maximize profit.
15. RL applications
RL is also instrumental in advancing autonomous vehicle technology,
enhancing the safety and efficiency of self-driving cars,
and minimizing accident risks.
16. RL applications
Additionally, RL is revolutionizing the way chatbots learn,
enhancing their conversational skills.
This leads to more accurate responses over time, thereby improving user experiences.
17. What's next?
In this course,
we'll delve into the fascinating foundations and principles of RL.
We'll learn how to identify, frame, and solve RL problems with a variety of techniques.
We'll also apply these techniques on real scenarios using the gymnasium library in Python.
18. Let's practice!
But before going there, let's put the ideas learned so far into practice!