Working with Deep Reinforcement Learning

Conventional Machine Learning works best when it is possible to find stable, representative labeled data from which it can find connections between the input features and the predictive outcomes. The effort to produce this labeled data is not always feasible or cost-effective. Reinforcement Learning systems mimic the successes of established learning approaches found in the fields of neuroscience and animal conditioning research. When mixed with the remarkably successful techniques of Deep Learning systems, the capacity to train agents in dynamic environments evolves to include the ability of “learning to learn”. This approach is expected to generalize too much wider problem spaces including sophisticated gameplay, online ad-placement, digital resource management, optimized control systems, and self-driving vehicles.

In addition to covering the main ideas of deep reinforcement learning, we will cover some of the main tools and frameworks as well as leveraging widely-used Python-based libraries students have probably already run into in machine learning spaces.

Course Summary

Purpose: 
This course will provide an introduction to deep reinforcement learning, what it is, how it works and how you can apply it to real-world problems.
Skill Level: 
Learning Style: 

Hands-on training is customized, instructor-led training with an in-depth presentation of a technology and its concepts, featuring such topics as Java, OOAD, and Open Source.

Hands On help

Workshops are instructor-led lab-intensives focused on the practical application of technologies through the facilitation of a project-related lab. Workshops are just the opposite of Seminars. They deliver the highest level of knowledge transfer of any format. Think wide (breadth) and deep (depth).

Workshop help
Duration: 
2 Days
Productivity Objectives: 
  • Define the concepts of agents, environments, states, actions, and rewards
  • Describe and use the major learning approaches of the fast-changing world of Deep reinforcement learning systems
  • Identify possible applications of deep reinforcement learning in your organization or industry

What You'll Learn

In the Working with Deep Reinforcement Learning training course you’ll learn:

  • Reinforcement Learning
    • Definitions & Terms
  • Q-Functions
    • State Actions
    • Complex Probability
    • Distributions of Reward
  • Policy Gradient Methods
  • Ray
  • OpenGym

Get Custom Training Quote

We'll work with you to design a custom Working with Deep Reinforcement Learning training program that meets your specific needs. A 100% guaranteed plan that works for you, your team, and your budget.

Learn More

Chat with one of our Program Managers from our Boulder, Colorado office to discuss various training options.

DevelopIntelligence has been in the technical/software development learning and training industry for nearly 20 years. We’ve provided learning solutions to more than 48,000 engineers, across 220 organizations worldwide.

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