The Conference for Machine Learning Innovation

Integrated Reinforcement learning and imitation learning using deep transfer learning Workshop: Part 2

Workshop
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Register until October 20:
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✓ Team discount
✓ Extra Specials for Freelancers
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Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos

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 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 originates from the archive of Diese Session stammt aus dem Archiv von BerlinBerlin . Take me to the program of . Hier geht es zum aktuellen Programm von Singapore Singapore .

This Session originates from the archive of Diese Session stammt aus dem Archiv von BerlinBerlin . Take me to the program of . Hier geht es zum aktuellen Programm von Berlin Berlin .

This Session originates from the archive of Diese Session stammt aus dem Archiv von BerlinBerlin . Take me to the program of . Hier geht es zum aktuellen Programm von Munich Munich .

This Session Diese Session originates from the archive of stammt aus dem Archiv von BerlinBerlin . Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

Behind the Tracks