Oliver Zeigermann

In a Half Day Hands-on Training at ODSC West in San Francisco in Autumn 2019 Oliver and Christian will show you details on how you can use reinforcement learning practically:

Practical Reinforcement Learning with TensorFlow 2.0 & TF-Agents – Hands on

In this workshop you will discover how machines can learn complex behaviors and anticipatory actions. Using this approach autonomous helicopters fly aerobatic maneuvers and even the GO world champion was beaten with it. A training dataset containing the “right” answers is not needed, nor is “hard-coded” knowledge. The approach is called “reinforcement learning” and is almost magical.

Using TF-Agents on top of TensorFlow 2.0 we will see how a real-life problem can be turned into a reinforcement learning task. In an accompanying Python notebook, we implement – step by step – all solution elements, highlight the design of Google’s newest reinforcement learning library, point out the role of neural networks and look at optimization opportunities.

Goals
• Basics of reinforcement learning
• When and when not to use it
• Design of TF-Agents on top of TensorFlow 2.0
• Hands-on Implementation

– Read Article online –