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researcher

M. Riedmiller

13 papers hereh-index 8268 citations20 works total

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

author position
  • first author1
  • middle author4
  • last author8

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.RO4
  • cs.AI2
same name
  • M. Riedmiller — 2 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedOffline Actor-Critic Reinforcement Learning Scales to Large Models

2 citations · 5 across the 13 of their papers we have counts for

collaborators
Showing 2024 · cs.LGShow all

3 papers · 2 filters

cs.LG2024

Learning from negative feedback, or positive feedback or both

Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari +9

Existing preference optimization methods often assume scenarios where paired preference feedback (preferred/positive vs. dis-preferred/negative examples) is available. This require…

cs.LG2024

Imitating Language via Scalable Inverse Reinforcement Learning

Markus Wulfmeier, Michael Bloesch, Nino Vieillard +13

The majority of language model training builds on imitation learning. It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning…

cs.LG2024★ 2 cited

Offline Actor-Critic Reinforcement Learning Scales to Large Models

Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang +9

We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning. We find that of…

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