What is RL?
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To get the most out of this course, you should have a solid understanding of mathematics (probability theory in particular). Familiarity with machine learning basics and NumPy will also be beneficial.
Reinforcement learning (RL) is a machine learning paradigm primarily focused on decision-making and control tasks, where an agent learns optimal strategies by interacting with an environment and maximizing cumulative rewards.
Reinforcement learning is heavily inspired by behavioral psychology, particularly how humans and animals learn through experiences. Just as a dog learns to sit when given treats for correct behavior, an RL agent learns by receiving rewards for its actions.
Agent and Environment
The agent is the decision-maker in the RL system. It observes the environment, selects actions, and learns from feedback to improve its future performance.
The environment represents everything that the agent interacts with. It responds to the agent's actions and provides feedback in the form of new states and rewards.
The agent is only responsible for making decisions — selecting actions based on its observations and learning from the resulting outcomes — while the environment dictates the rules of interaction.
Applications of RL
Reinforcement learning is widely used in various fields where decision-making under uncertainty is crucial. Some key applications include:
- Robotics: RL helps robots learn complex tasks such as grasping objects, locomotion, and industrial automation;
- Gaming AI: RL powers AI agents in games like chess, Go, and Dota 2, achieving superhuman performance;
- Finance: RL optimizes trading strategies, portfolio management, and risk assessment;
- Healthcare: RL aids in personalized treatment plans, robotic surgery, and drug discovery;
- Autonomous systems: RL enables self-driving cars, drones, and adaptive traffic control systems;
- Recommendation systems: RL helps improve personalized content recommendations in streaming platforms and e-commerce.
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