Deep Reinforcement Learning Tutorial

This tutorial will guide you through the basics of Deep Reinforcement Learning (DRL), covering the fundamental concepts, algorithms, and applications. DRL is a rapidly evolving field with numerous real-world applications.

Basic Concepts

• Reinforcement Learning (RL): A type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal.
• Deep Learning (DL): A subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.

Algorithms

• Q-Learning: An algorithm that learns to map states to actions by estimating the expected future rewards.
• Policy Gradient Methods: Algorithms that directly learn the policy (probability distribution over actions) that maximizes the expected reward.

Applications

• Robotics: Controlling robots to perform tasks such as manipulation and navigation.
• Games: Playing games like chess, Go, and poker.
• Autonomous Vehicles: Developing self-driving cars and drones.

Resources

• Deep Reinforcement Learning by David Silver - A comprehensive course on DRL.
• DeepMind Papers - Research papers from DeepMind, a leading company in DRL.

Deep Reinforcement Learning

To learn more about the applications of DRL, check out our DRL Applications page.