Reinforcement Learning

4 hours of instruction

This course covers the specialized branch of machine learning and deep learning called reinforcement learning (RL). By the end of this course students will be able to define RL use cases and real world scenarios where RL models are used, they will be able to create a simple RL model and evaluate its performance.

OBJECTIVES

1. Discuss the theory behind reinforcement learning and use cases
2. Apply reinforcement learning theory to deep learning models
3. Implement a reinforcement learning model using TensorFlow

PREREQUISITES

Advanced CNN

SYLLABUS & TOPICS COVERED

1. Reinforcement Learning Overview
   • Reinforcement learning (RL) use cases
   • Theoretical concepts behind RL
2. Reinforcement Learning Implementation
   • Set up of the RL testing environment
   • Implementation of an RL model using TensorFlow
   • Evaluation of the RL model performance and next steps

SOFTWARE REQUIREMENTS

OpenGL, You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.