The five principles of Reinforcement Learning
Let's begin building the first pillars of your intuition into how Reinforcement Learning works. These are the fundamental principles of Reinforcement Learning, which will get you started with the right, solid basics in AI.
Here are the five principles:
- Principle #1: The input and output system
- Principle #2: The reward
- Principle #3: The AI environment
- Principle #4: The Markov decision process
- Principle #5: Training and inference
In the following sections, you can read about each one in turn.
Principle #1 – The input and output system
The first step is to understand that today, all AI models are based on the common principle of inputs and outputs. Every single form of Artificial Intelligence, including Machine Learning models, ChatBots, recommender systems, robots, and of course Reinforcement Learning models, will take something as input, and will return another thing as output.
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