The Conference for Machine Learning Innovation

Human and Multi-agent systems

Session
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Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
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Join the ML Revolution!
Register until August 11:
✓ Save up to $593
✓ ML Intro Day for free
✓ Team discount
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Join the ML Revolution!
Register until August 11:
✓ Save up to $593
✓ ML Intro Day for free
✓ Team discount
Register Now
Join the ML Revolution!
Register until September 23:
✓ PS Classic or C64 Mini for free
✓ Save up to €310
10 % Team Discount
Register Now
Join the ML Revolution!
Register until September 23:
✓ PS Classic or C64 Mini for free
✓ Save up to €310
10 % Team Discount
Register Now
Infos

Human / AI interaction loop training as a new approach for interactive learning with reinforcement-learning: Reinforcement-Learning (RL) in various decision-making tasks of Machine-Learning (ML) provides effective results with an agent learning from a stand-alone reward function. However, it presents unique challenges with large amounts of environment states and action spaces, as well as in the determination of rewards. This complexity, coming from high dimensionality and continuousness of the environments considered herein, calls for a large number of learning trials to learn about the environment through RL. Imitation-Learning (IL) offers a promising solution for those challenges, using a teacher’s feedback. In IL, the learning process can take advantage of human-sourced assistance and/or control over the agent and environment. In this study, we considered a human teacher and an agent learner. The teacher takes part in the agent’s training towards dealing with the environment, tackling a specific objective, and achieving a predefined goal. Within that paradigm, however, existing IL approaches have the drawback of expecting extensive demonstration information in long-horizon problems. With this work, we propose a novel approach combining IL with different types of RL methods.

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

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

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

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