Become a leader in the field of reinforcement learning and create innovative and effective solutions in various fields. Join the future of technology and innovation!”

Reinforcement Learning is fundamental in the creation of innovative and effective solutions in various fields. It is used in robotics to create motion control systems and in artificial intelligence to improve decision making. It is also used in the development of video games and in the optimization of energy efficiency in buildings. In addition, it offers an opportunity for engineers to develop highly specialized skills that are highly demanded in the industry such as policy gradient optimization, creation of OpenAI environments, neural network-based credit evaluation, and implementation of reinforcement learning algorithms.

The Postgraduate certificate in Reinforcement Learning is a response to the current needs of industry and technology in relation to reinforcement learning. This field is fundamental in the creation of algorithms that optimize results, providing competitive advantages to companies that integrate its application. Policy gradient optimization, which is used to optimize neural network policies, is also taught. Therefore, this university qualification has been designed to offer engineers the opportunity to develop theoretical and practical skills to solve complex problems and create innovative solutions.

The Reinforcement Learning program is delivered in a 100% online format, allowing students to learn at their own pace and adapt to their schedules. Relearning methodology is used to provide an effective and unique learning experience. Students have access to OpenAI environments, allowing them to experiment and learn about creating them and using reinforcement learning algorithms. Time difference learning and Q-Learning is fundamental to reinforcement learning and is addressed throughout the program.

It is a program that offers a unique and effective learning experience, taught in a 100% online format and using the Relearning methodology. This allows students to distribute the course load according to their schedules and to be able to combine it with other areas of their lives. In addition, you will have access to a virtual campus full of theoretical, practical and additional content that will facilitate the integration of knowledge and which can be accessed 24 hours a day, 365 days a year.

You will obtain a recognized university qualification that will increase your employment opportunities and salaries”

This Postgraduate certificate in Reinforcement Learning contains the most complete and up-to-date program on the market. Its most outstanding features are:

• The development of case studies presented by experts in Reinforcement Learning
• The graphic, schematic, and practical contents with which they are created, provide practical information on the 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

You will learn autonomously and collaboratively, using a variety of resources, from readings and videos to tutorials and practical projects”

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

Its multimedia content, developed with the latest educational technology, will allow the professional a situated and contextual learning, that is, a simulated environment that will provide an immersive training programmed to train in real situations.  

The design of this program focuses on Problem-Based Learning, in which the professional will have to try to solve the different professional practice situations that will arise throughout the academic course. For this purpose, the student will be assisted by an innovative interactive video system created by renowned experts.

You will have access to practical and challenging projects that will allow you to apply your knowledge and demonstrate your skills"

The Relearning methodology will allow you to consolidate and apply your knowledge effectively and efficiently"

The syllabus of the Postgraduate certificate in Reinforcement Learning is the most cutting-edge of the current academic panorama, and addresses relevant topics in the field of reinforcement learning such as policy gradient optimization, credit evaluation based on neural networks, and the implementation of reinforcement learning algorithms. Throughout the program, the theoretical approach is combined with the practical application of the acquired knowledge in challenging projects and real applications, allowing students to acquire a deep and complete understanding of the concepts and techniques of reinforcement learning.

A syllabus with which you will acquire highly specialized skills that are valued in the industry”

Module 1. Reinforcement Learning

1.1. Optimization of Rewards and Policy Searching

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 Searching
1.3.2. Deep Learning Policies
1.3.3. Neural Networks Policy Expansion

1.4. Stock Assessment: the Problem of Credit Allocation

1.4.1. Risk Analysis for Credit Allocation
1.4.2. Estimation of Loan Profitability
1.4.3. Credit Assessment 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 Gradients 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. Deep Q-Learning Implementation
1.8.3. Deep Q-Learning Variations

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. Reinforcement Learning Algorithm Implementation
1.10.3. Reinforcement Learning Algorithm Assessment

You will expand your horizons and become an expert in Reinforcement Learning”