Deep Reinforcement Learning

Welcome to Deep Reinforcement Learning

Welcome to the Deep Reinforcement Learning Nanodegree program!

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Get Help with Your Account

What to do if you have questions about your account or general questions about the program.

Learning Plan

Obtain helpful resources to accelerate your learning in this first part of the Nanodegree program.

Introduction to RL

Reinforcement learning is a type of machine learning where the machine or software agent learns how to maximize its performance at a task.

The RL Framework: The Problem

Learn how to mathematically formulate tasks as Markov Decision Processes.

The RL Framework: The Solution

In reinforcement learning, agents learn to prioritize different decisions based on the rewards and punishments associated with different outcomes.

Monte Carlo Methods

Write your own implementation of Monte Carlo control to teach an agent to play Blackjack!

Temporal-Difference Methods

Learn about how to apply temporal-difference methods such as SARSA, Q-Learning, and Expected SARSA to solve both episodic and continuing tasks.

Solve OpenAI Gym's Taxi-v2 Task

With reinforcement learning now in your toolbox, you're ready to explore a mini project using OpenAI Gym!

RL in Continuous Spaces

Learn how to adapt traditional algorithms to work with continuous spaces.

What's Next?

In the next parts of the Nanodegree program, you'll learn all about how to use neural networks as powerful function approximators in reinforcement learning.

Study Plan

This lesson covers the study plan and prerequisites for this course.

Deep Q-Networks

Extend value-based reinforcement learning methods to complex problems using deep neural networks.

Project: Navigation

Train an agent to navigate a large world and collect yellow bananas, while avoiding blue bananas.

Study Plan

Obtain helpful resources to accelerate your learning in the third part of the Nanodegree program.

Introduction to Policy-Based Methods

Policy-based methods try to directly optimize for the optimal policy.

Policy Gradient Methods

Policy gradient methods search for the optimal policy through gradient ascent.

Proximal Policy Optimization

Learn what Proximal Policy Optimization (PPO) is and how it can improve policy gradients. Also learn how to implement the algorithm by training a computer to play the Atari Pong game.

Actor-Critic Methods

Miguel Morales explains how to combine value-based and policy-based methods, bringing together the best of both worlds, to solve challenging reinforcement learning problems.

Deep RL for Finance (Optional)

Learn how to apply deep reinforcement learning techniques for optimal execution of portfolio transactions.

Continuous Control

Train a double-jointed arm to reach target locations.

Study Plan

Obtain helpful resources to accelerate your learning in the fourth part of the Nanodegree program.

Introduction to Multi-Agent RL

Case Study: AlphaZero

Collaboration and Competition

Train a pair of agents to play tennis.

Dynamic Programming

The dynamic programming setting is a useful first step towards tackling the reinforcement learning problem.

Neural Networks

Review the basics of neural networks.

Convolutional Neural Networks

Review the basics of convolutional neural networks.

Deep Learning with PyTorch

Learn how to use PyTorch for building deep learning models.

Udacity Workspaces

Learn how to use Workspaces in the Udacity classroom.

C++ Getting Started

The differences between C++ and Python and how to write C++ code.

C++ Vectors

To program matrix algebra operations and translate your Python code, you will need to use C++ Vectors. These vectors are similar to Python lists, but the syntax can be somewhat tricky.

Practical C++

Learn how to write C++ code on your own computer and compile it into a executable program without running into too many compilation errors.

C++ Object Oriented Programming

Learn the syntax of C++ object oriented programming as well as some of the additional OOP features provided by the language.

Python and C++ Speed

In this lesson, we'll compare the execution times of C++ and Python programs.

C++ Intro to Optimization

Optimizing C++ involves understanding how a computer actually runs your programs. You'll learn how C++ uses the CPU and RAM to execute your code and get a sense for what can slow things down.

C++ Optimization Practice

Now you understand how C++ programs execute. It's time to learn specific optimization techniques and put them into practice. This lesson will prepare you for the lesson's code optimization project.

Project: Optimize Histogram Filter

Get ready to optimize some C++ code. You are provided with a working 2-dimensional histogram filter; your job is to get the histogram filter code to run faster!

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