The Conference for Machine Learning Innovation

Reinforcement Learning and Imitation Learning Workshop

Workshop
Join the ML Revolution!
Register until October 15:
✓Save up to 223 €
✓10 % Team Discount
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Join the ML Revolution!
Register until October 15:
✓Save up to 223 €
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Infos
Booking note:
Reinforcement Learning Workshop

Deep Reinforcement-Learning (RL) in various decision-making tasks of Machine-Learning (ML) provides effective results with an agent/agents learning from observing an environment and gaining rewards and punishments. RL as a great technique in ML shows its ability to be utilized in different time-series and Computer Vision-based (CV) projects like autonomous driving, robotics, traffic control, web system configuration, recommendation systems and game applications. In complex environments where RL underperforms as a consequence of extensive demonstration information in long-horizon problems, Imitation Learning (IL) offers a promising solution for the challenges. In IL, the learning process can take advantage of human-sourced assistance and/or control over the agent and environment. The purpose of this talk is to provide an introduction to Deep RL and IL at a level easily understood by students and researchers in a wide range of disciplines. Also, we will overview different RL and IL techniques and methods as long as discuss how they are utilized to improve the performance of different tasks. Also, we cover the usage of RL and IL in CV, NLP and time-series tasks.

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

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

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

This Session belongs to the Diese Session gehört zum Programm vom MunichMunich 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 MunichMunich 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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