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Denis Steckelmacher — most-cited papers & profile · Reinforcement Learning
← authors
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overview
Denis Steckelmacher
12
papers ·
46
citations ·
8
h-index
Google Scholar ↗
Semantic Scholar ↗
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Most-cited papers
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
2018 · 41 citations
The Actor-Advisor: Policy Gradient With Off-Policy Advice
2019 · 3 citations
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice
2018 · 1 citations
Transfer Learning Across Simulated Robots With Different Sensors
2019 · 1 citations
Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models
2026
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
2017
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
2019
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem
2023
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth
2023
Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution
2024
Top co-authors
Ann Now\'e
· 10
H\'el\`ene Plisnier
· 6
Diederik M. Roijers
· 5
Senne Deproost
· 2
Anna Harutyunyan
· 1
Axel Abels
· 1
Bruno Depraetere
· 1
Diederik Roijers
· 1
Jeroen Willems
· 1
Peter Vrancx
· 1
Qingshuang Sun
· 1
Rapha\"el Avalos
· 1
Topics
Policy Gradient
Model-Based RL
Value-Based
Meta-RL
Exploration
Safe RL
Multi-Agent
RLHF & Alignment
Offline RL