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139. PDG: A Unified Game-Theoretic Approach to Multiagent RL

Februar 27 @ 6:00 pm

Machine Learning Paper Discussion Group

In the Paper Discussion Group (PDG) we discuss on a weekly base recent and fundamental papers in the area of machine learning. For several weeks, we follow one track to dive a bit deeper into a topic by reading matching or correlate papers. If you are interested, read the paper and join us.

Track: AlphaStar
Topic: A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Paper: https://arxiv.org/abs/1711.00832

The potential next papers:
Attention Is All You Need
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
DEEP REINFORCEMENT LEARNING WITH RELATIONAL
INDUCTIVE BIASES
Pointer Networks
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Human-level control through deep reinforcement learning
Counterfactual Multi-Agent Policy Gradients
Population Based Training of Neural Networks
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

[Wir treffen uns im Informatik-Gebäude des KIT (50.34), Raum -120. Wenn alle Teilnehmer Mutterspachler sind, sind die Diskussionen auf deutsch.]

Details

Datum:
Februar 27
Zeit:
6:00 pm
Website:
http://www.ml-ka.de

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