With this 100% online Postgraduate certificate, you will develop in your work the most innovative Reinforcement Learning algorithms, solving real-world problems in fields such as process control"

In order to provide high quality goods or services, experts need to have at their fingertips mechanisms to explore their operation and evaluate both their performance and efficiency. In this sense, the creation of OpenAI environments provides an accessible framework for them to develop, test and compare intelligent algorithms. Such platforms facilitate experimentation by providing simulations of real-world scenarios where AI agents can interact and learn. As a result, specialists can train systems such as autonomous vehicles or drones before their market launch.

In this context, TECH is launching a pioneering program in Reinforcement Learning. Through it, students will gain practical skills to develop the most efficient algorithms and apply Neural Network Policies optimally to their projects. The curriculum will delve into issues ranging from reward optimization to action evaluation and gradient optimization. At the same time, the syllabus will delve into Markov Decision Processes in order for the graduates to solve sequential decision making problems under uncertainty frameworks.  In addition, the program will include case studies that will enhance the students' skills in aspects such as the construction of Deep Neural Networks for Deep Q-Learning.

The university program will be based on the revolutionary Relearning methodology, of which it is a pioneer. This teaching system consists of the continuous reiteration of the most important concepts, allowing students to consolidate them in an agile and natural way. In this way, it will not be necessary for them to invest long hours of study or resort to arduous traditional techniques such as memorization. Undoubtedly, a unique opportunity to study a program in comfort. All that is required is students to have a device with Internet access to view the content at any time of the day, hosted in the Virtual Campus. 

Optimize Q-Learning parameters at the world's best digital university according to Forbes”

This Postgraduate certificate in Reinforcement Learning contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Reinforcement learning
• The graphic, schematic and practical contents of the program provide Sports and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies 
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Looking to delve deeper into Markov Decision Processes?  Master this mathematical framework with this university program in only 6 weeks”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities. 

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations. 

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.  

You will enrich your practice with the most advanced Reward Learning Algorithms and specialize agents to make optimal decisions in dynamic environments"

With TECH's Relearning system you will have a natural and flexible intellectual growth. Forget about investing long hours of study!"

This program will provide students with a comprehensive view of Reinforcement Learning in order to enrich their projects. To achieve this, the curriculum will analyze key concepts such as reward optimization or policy search. Throughout the program, students will acquire practical skills for both the optimal creation of OpenAI environments and the construction of Deep Neural Networks for Deep Q-Learning. This will provide graduates with the necessary resources to address Reinforcement Learning problems, specifically in environments with high complexity or dimensionality, such as video games.  

A university program designed by experts in Machine Learning that will ensure a quality leap in your professional career"

Module 1. Reinforcement Learning

1.1. Optimization of Rewards and Policy Search

1.1.1. Reward Optimization Algorithms
1.1.2. Policy Search Processes
1.1.3. Reinforcement Learning for Reward Optimization

1.2. OpenAI

1.2.1. OpenAI Gym Environment
1.2.2. Creation of OpenAI Environments
1.2.3. Reinforcement Learning Algorithms in OpenAI

1.3. Neural Network Policies

1.3.1. Convolutional Neural Networks for Policy Search
1.3.2. Deep Learning Policies
1.3.3. Extending Neural Network Policies

1.4. Stock Evaluation: the Credit Allocation Problem

1.4.1. Risk Analysis for Credit Allocation
1.4.2. Estimating the Profitability of Loans
1.4.3. Credit Evaluation Models based on Neural Networks

1.5. Policy Gradients

1.5.1. Reinforcement Learning with Policy Gradients
1.5.2. Optimization of Policy Gradients
1.5.3. Policy Gradient Algorithms

1.6. Markov Decision Processes

1.6.1. Optimization of Markov Decision Processes
1.6.2. Reinforcement Learning for Markov Decision ¨Processes
1.6.3. Models of Markov Decision Processes

1.7. Temporal Difference Learning and Q-Learning

1.7.1. Application of Temporal Differences in Learning
1.7.2. Application of Q-Learning in Learning
1.7.3. Optimization ofQ-LearningParameters

1.8. Implementation of Deep Q-Learning  and Deep Q-Learning  variants

1.8.1. Construction of Deep Neural Networks for  Deep Q-Learning
1.8.2. Implementation of Deep Q-Learning
1.8.3. Variations of Deep Q-Learning

1.9. Reinforment Learning Algorithms

1.9.1. Reinforcement Learning Algorithms
1.9.2. Reward Learning Algorithms
1.9.3. Punishment Learning Algorithms

1.10. Design of a Reinforcement Learning Environment. Practical Application

1.10.1. Design of a Reinforcement Learning Environment
1.10.2. Implementation of a Reinforcement  Learning Algorithm
1.10.3. Evaluation of a Reinforcement Learning Algorithm

Reach your maximum efficiency in the field of Reinforcement Learning thanks to the most innovative pedagogical resources offered by TECH. Enroll now!”