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researcher

M. Pallasch

2 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2

Across the 2 of 2 papers where every author was matched, so the position is known.

fields
  • cs.LG2

identity via Semantic Scholar / OpenAlex

most citedGeneralization, Mayhems and Limits in Recurrent Proximal Policy Optimization

10 citations · 10 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2022

On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer

Marco Pleines, Konstantin Ramthun, Yannik Wegener +12

Autonomously trained agents that are supposed to play video games reasonably well rely either on fast simulation speeds or heavy parallelization across thousands of machines runnin…

cs.LG2022★ 10 cited

Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization

Marco Pleines, Matthias Pallasch, Frank Zimmer +1

At first sight it may seem straightforward to use recurrent layers in Deep Reinforcement Learning algorithms to enable agents to make use of memory in the setting of partially obse…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.