Improve your skills on Markov Decision Processes or Q-Learning Parameter Optimization, thanks to TECH, the world's largest digital university”

Reinforcement Learning is considered one of the most promising fields of artificial intelligence for the future. The ability to learn on its own from a machine is increasingly important in a world where the volume of data is constantly increasing and the speed of decision making is crucial.

For this reason, TECH has designed a Postgraduate certificate in Reinforcement Learning with which it seeks to provide students with the necessary skills and competencies to be able to perform their work as specialists, with the highest possible quality in their jobs. Thus, throughout this program, aspects such as Markov Decision Process Models, Reinforcement Learning Algorithms, Policy Gradients or the OpenAI Gym Environment will be addressed.

All this, through a convenient 100% online mode that allows students to organize their schedules and studies, combining them with their other interests. In addition, this degree has the most complete theoretical and practical materials on the market, which facilitates the student's study process and allows him/her to reach their most demanding objectives

Become an expert in Reinforcement Learning in only 6 weeks and with total freedom of organization”

This Postgraduate certificate in Reinforcement Learning contains the most complete and up-to-date educational 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 self-assessment can be used 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

Improve your professional profile to the maximum and achieve success in one of the most promising areas in the IT field, thanks to TECH”

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

Its 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 an immersive education programmed to learn in real situations.

The design of this program focuses on Problem-Based Learning, by means of which the professional must try to solve the different professional practice situations that are presented throughout the academic course. This will be done with the help of an innovative system of interactive videos made by renowned experts.

Delve into Deep Learning Policies and Reward Learning Algorithms from the comfort of your home at any time of the day"

Access all the content on Evaluating a Reinforcement Learning Algorithm from your Tablet, mobile or computer"

The didactic resources of this syllabus have been designed by the renowned professionals that make up TECH's team of experts in the area of Computer Science. These specialists have used their extensive experience and their most advanced knowledge to create practical and updated contents. All this, based on the most efficient teaching methodology in the market, TECH's Relearning.

The most specialized and complete vision of the academic market can be found in this TECH syllabus”

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 of Q-Learning parameters

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. Reinforcement 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

Thanks to the most efficient pedagogical methodology, you will be able to acquire new knowledge in a precise way and in only 150 hours”