Overview

Students from the Deep Reinforcement Learning program at Udacity learn to implement classical solution methods, define Markov decision processes, policies, and value functions, and derive Bellman equations. They learn dynamic programming, Monte Carlo methods, temporal-difference methods, deep RL, and apply these techniques to solve real-world problems. They learn to train agents to navigate virtual worlds, generate optimal financial trading strategies, and apply RL to multiple interacting agents.

Nanodegree Skills

Introduction to Deep Reinforcement Learning

Exploration-exploitation dilemma • Markov decision processes • Multi-armed bandit problems • Bellman equation • Policy-based reinforcement learning • Continuous functions • Value-based reinforcement learning • Monte carlo methods • Dynamic programming

Value Based Methods

Value-based reinforcement learning • Prioritized experience replay • Deep q-networks • Double deep q-networks • Dueling deep q-networks

Policy-Based Methods

Stochastic policy gradients • Reinforce algorithm • Policy optimization algorithms • Evolutionary algorithms • Monte carlo policy gradients • Generalized advantage estimation

Multi-Agent Reinforcement Learning

Multi-agent training • Markov games • Alphazero

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Programme Structure

Courses include:

Value Based Methods
Policy-Based Methods
Multi-Agent Reinforcement Learning
Computing Resources
Neural Networks in PyTorch,
C++ Programming

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Key information

Duration

Part-time
- 4 months
- Flexible

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online

Disciplines

Artificial Intelligence Machine Learning View 323 other programmes in Machine Learning in United States

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Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

Prerequisites:

Intermediate Python
Deep learning framework proficiency
Neural network basics
Object-oriented programming basics
Reinforcement learning fundamentals

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Tuition Fee

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International

249 USD/module

Tuition Fee

Based on the tuition of 249 USD per module during 4 months.
National

249 USD/module

Tuition Fee

Based on the tuition of 249 USD per module during 4 months.
In-State

249 USD/module

Tuition Fee

Based on the tuition of 249 USD per module during 4 months.

249$ - Monthly Subscription - Unlimited access to all Udacity programs, skills and projects

Funding

Win a €10000 Scholarship! Deadline: 30 June 2020

Embark on your journey with the Studyportals Scholarship! We're giving away a total of 20,000 euros to help you achieve your dream of studying abroad.

Win a €10000 Scholarship! Deadline: 30 June 2020

Embark on your journey with the Studyportals Scholarship! We're giving away a total of 20,000 euros to help you achieve your dream of studying abroad.

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Scholarships Information

Below you will find Master's scholarship opportunities for Deep Reinforcement Learning.

Available Scholarships

You are eligible to apply for these scholarships but a selection process will still be applied by the provider.

About

Overview

Nanodegree Skills

Get more details

Programme Structure

Check out the full curriculum

Key information

Duration

Start dates & application deadlines

Language

Delivered

Disciplines

Explore more key information

What students do after studying

Academic requirements

English requirements

Other requirements

General requirements

Make sure you meet all requirements

Tuition Fee

International

National

In-State

Funding

Scholarships Information

Available Scholarships

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