One of the most important types of machine learning is Reinforcement Learning.
While everyone works on Supervised or Unsupervised data models, Reinforcement Learning is the most important learning method.
Reinforcement Machine Learning
Reinforcement works like the learner or decision-maker technique. All AI-ML models work with algorithms and command-taking processes, but reinforcement works on self-decision-making, like humans.
Reinforcement Learning first allows the machine to learn an environment, receive feedback, and check what the issue is, how to handle it, and how to resolve it. If the machine has already worked on the same task, the Reinforcement Learning model does not take much time to repeat the same process. This is because RL is a self-learning model.
- Environment: Set up all algorithms and models.
- State: The work problem statement.
- Action: Take the decision by the machine.
- Reward: Receive the feedback and resolve through self-learning.
Real-World Applications of Reinforcement Learning:
- Robotics: RL is used to automate tasks in environments like manufacturing companies. Robots learn to optimize task processes and improve efficiency.
- Games: Super Reinforcement Learning algorithms are used to create complex games like chess, Go, and video games.
Movie Example: One of the famous FreeGuy Movie works around the Machine Learning and Reinforcement Learning Model.
In the movie, Guy is a Bot or NPC player in a video game, and the movie's name is FreeGuy.
Initially, Guy is a Bot in the game and works the same routine and tasks in the game daily, But one day, Guy picks up the real player Google and Wear and makes a decision to change his model or build self algoritums.
So if we connect the real world, this game and reinforcement model
Guy journey principles of reinforcement learning
- Agent: Guy is the agent, learns and makes decisions.
- Environment: Free Guy City is an environment guy who lives and works in this city.
The guy's current situation in the game
- Action: Guy's choices, like wearing sunglasses, helping others, or fighting back.
- Reward: Positive responses reinforce Guy's actions and encourage him to repeat them.
Self-Learning
- Guy starts as a simple bot player but evolves into a more complex character through Machine learning from his experiences. This reflects how reinforcement learning allows machines to improve their decision-making.