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Introduction to Reinforcement Learning
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Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

1. RL Core Theory
What is RL?RL vs Other Learning ParadigmsMarkov Decision ProcessEpisodes and Returns Model, Policy, and ValuesExploration vs ExploitationGymnasium BasicsChallenge: Setting Up an Environment
2. Multi-Armed Bandit Problem
Problem IntroductionAction ValuesEpsilon-Greedy AlgorithmUpper Confidence Bound AlgorithmGradient Bandits AlgorithmChallenge: Multi-Armed Bandits
3. Dynamic Programming
What is Dynamic Programming?Bellman EquationsOptimality ConditionsPolicy EvaluationPolicy ImprovementGeneralized Policy IterationPolicy IterationValue IterationChallenge: Dynamic Programming
4. Monte Carlo Methods
What are Monte Carlo Methods?Value Function EstimationMonte Carlo ControlExploration ApproachesOn-Policy Monte Carlo ControlOff-Policy Monte Carlo ControlIncremental ImplementationsChallenge: Monte Carlo Methods
5. Temporal Difference Learning
What is Temporal Difference Learning?TD(0): Value Function Estimation SARSA: On-Policy TD LearningQ-Learning: Off-Policy TD LearningGeneralization of TD Learning

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What is RL?

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 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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To which task would you apply reinforcement learning?

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Section 1. Chapitre 1
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