Deep Reinforcement Learning with Python Training Course

Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.

This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

Course Outline

• Introduction
• Reinforcement Learning Basics
• Basic Reinforcement Learning Techniques
• Introduction to BURLAP
• Convergence of Value and Policy Iteration
• Reward Shaping
• Exploration
• Generalization
• Partially Observable MDPs
• Options
• Logistics
• TD Lambda
• Policy Gradients
• Deep Q-Learning
• Topics in Game Theory
• Summary and Next Steps

Contact us

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