The Conference for Machine Learning Innovation

Playing Doom with TF-Agents and TensorFlow 2.0

Session
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Register until September 10:
✓ Raspberry Pi or C64 Mini for free
✓Save up to 313 €
✓10 % Team Discount
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Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
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✓10% Team Discount
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Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Infos
Wednesday, September 9 2020
14:15 - 15:00

During recent years, enormous advances in Reinforcement Learning (RL) have been showcased by DeepMind, OpenAI and others. Their AIs achieve similar-to-human or even super-human capabilities in various games including Atari games, the Chinese board game Go, Dota 2 and Starcraft. Although major Deep Learning frameworks reached new levels of maturity and usability when employing Supervised Learning, developing RL solutions often remains hard to get started and even harder to get right. In this talk we will look at the new TF-Agents framework simplifying development of RL solutions with TensorFlow 2.0 drastically. On the example of an AI playing Doom, we will present the relevant steps and code parts to get you started.

This Session belongs to the Diese Session gehört zum Programm vom SingaporeSingapore program. Take me to the program of . Hier geht es zum Programm von Online Edition Online Edition .

This Session belongs to the Diese Session gehört zum Programm vom SingaporeSingapore program. Take me to the program of . Hier geht es zum Programm von Munich Munich .

Take me to the full program of Zum vollständigen Programm von Singapore Singapore .

This Session belongs to the Diese Session gehört zum Programm vom SingaporeSingapore program. Take me to the program of . Hier geht es zum Programm von Berlin Berlin .

This Session Diese Session belongs to the gehört zum Programm von SingaporeSingapore program. Take me to the current program of . Hier geht es zum aktuellen Programm von Online Edition Online Edition , Munich Munich , Singapore Singapore or oder Berlin Berlin .

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