The Conference for Machine Learning Innovation

Integrated Reinforcement learning and imitation learning using deep transfer learning Workshop: Part 1

Workshop
Join the ML Revolution!
Register until conference starts:
✓ 2 in 1 conference special
✓ 10 % Team Discount
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Join the ML Revolution!
Register until conference starts:
✓ 2 in 1 conference special
✓ 10 % Team Discount
Register Now
Join the ML Revolution!
Register until the conference starts:
✓ 2-in-1 conference special
✓ 10 % Team Discount
Register Now
Join the ML Revolution!
Register until the conference starts:
✓ 2-in-1 conference special
✓ 10 % Team Discount
Register Now
Thank you for attending!
Register Now
Thank you for attending!
Register Now
Infos
Thursday, December 9 2021
10:00 - 13:00
Room:
Remote only

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.

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

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

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

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