Зміст курсу

Introduction to Reinforcement Learning

1. RL Core Theory

What is RL?RL vs Other Learning Paradigms Markov Decision Process Episodes and Returns Model, Policy, and Values Exploration vs Exploitation Gymnasium Basics Challenge: Setting Up an Environment

2. Multi-Armed Bandit Problem

Problem Introduction Action Values Epsilon-Greedy Algorithm Upper Confidence Bound Algorithm Gradient Bandits Algorithm Challenge: Multi-Armed Bandits

3. Dynamic Programming

What is Dynamic Programming?Bellman Equations Optimality Conditions Policy Evaluation Policy Improvement Generalized Policy Iteration Policy Iteration Value Iteration Challenge: Dynamic Programming

4. Monte Carlo Methods

What are Monte Carlo Methods?Value Function Estimation Monte Carlo Control Exploration Approaches On-Policy Monte Carlo Control Off-Policy Monte Carlo Control Incremental Implementations Challenge: Monte Carlo Methods

5. Temporal Difference Learning

What is Temporal Difference Learning?TD(0): Value Function Estimation SARSA: On-Policy TD Learning Q-Learning: Off-Policy TD Learning Generalization of TD Learning Challenge: Temporal Difference Learning

What is RL?

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.

Передумови

Definition

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

Definition

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.

Definition

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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Зміст курсу

Introduction to Reinforcement Learning

1. RL Core Theory

What is RL?RL vs Other Learning Paradigms Markov Decision Process Episodes and Returns Model, Policy, and Values Exploration vs Exploitation Gymnasium Basics Challenge: Setting Up an Environment

2. Multi-Armed Bandit Problem

Problem Introduction Action Values Epsilon-Greedy Algorithm Upper Confidence Bound Algorithm Gradient Bandits Algorithm Challenge: Multi-Armed Bandits

3. Dynamic Programming

What is Dynamic Programming?Bellman Equations Optimality Conditions Policy Evaluation Policy Improvement Generalized Policy Iteration Policy Iteration Value Iteration Challenge: Dynamic Programming

4. Monte Carlo Methods

5. Temporal Difference Learning

What is RL?

Передумови

Definition

Agent and Environment

Definition

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.

Definition

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.

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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Секція 1. Розділ 1