Reinforcement learning has been getting a lot of attention in the AI community lately because of its many applications, from playing video games to predicting stock prices. Reinforcement learning is everywhere, so no matter what type of AI you're interested in, there's no reason not to learn it. The goal of this article is to give you a solid understanding of the basics of reinforcement learning and explain some basic terminology you'll hear whenever you talk about RL. So let's get started.
- In reinforcement learning, an AI uses what's called time-delayed labeling to learn how to best interact in a real-time environment. Rewards As a signal.
- Through interaction with the environment, the AI learns a policy that returns the action with the highest reward for a given state.
- of Markov Decision Processes A mathematical framework for defining reinforcement learning problems. state, Actions and Rewards.
- We try to move from a state S to another state S' (which can be the same as S) and take an action 'A' that maximizes the reward 'R'. Very simple.
- RL is mainly Dynamic programming.
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- The class of algorithm.
- It tries to simplify complex problems by breaking them down into sub-problems and solving each problem recursively.
Now, let’s discuss one of the most widely used equations in reinforcement learning courses.
Bellman, also known as the father of dynamic programming, introduced one of the most important concepts in RL: equations. We discussed states above. Every state has some value, which can be evaluated using the Bellman equation. The Bellman equation is expressed as follows:
- here V(s) Represents a particular state value.
- Max(a) It's the action we take that will give us the maximum reward. Previously this was calculated with a brute force approach, but thanks to deep learning it can now be calculated in advance very easily.
- Gamma is Discount factor.
- Consider where we are right now: if we take the best action now, what long-term rewards can we expect at each step after that?
- what value Of the state?
- Helps with evaluation Expected Rewards in relation to the advantages or disadvantages of each state.
- Very important Hyperparameters Adjust for best results.
- The success value range is 0.9 and 0.99
- The lower the value, Short term thinking.
- The higher the value, Long-term rewards.
The terminal state has a value of zero.
That's it for the very basics of RL in this post. In the next post we will cover MDPs (Markov Decision Processes), which is a very important aspect of RL. I hope it wasn't too much to understand at once and you were able to get familiar with the basic terminology used in RL. If it was helpful, please give me a clap. Thanks for reading and please follow me. twitter Stay tuned for the next post and feel free to explore my work on GitHub. Have a great day!
