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Wolfram Burgard — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Wolfram Burgard
32
papers ·
507
citations ·
113
h-index
Lutheran University of Applied Sciences Nuremberg
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
2018 · 83 citations
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
2016 · 70 citations
Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments
2016 · 33 citations
Affordance Learning from Play for Sample-Efficient Policy Learning
2022 · 29 citations
Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning
2021 · 28 citations
Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration
2019 · 19 citations
VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control
2018 · 16 citations
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
2016 · 8 citations
Latent Plans for Task-Agnostic Offline Reinforcement Learning
2022 · 5 citations
Pre-training of Deep RL Agents for Improved Learning under Domain Randomization
2021 · 2 citations
Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving
2024 · 2 citations
Refined Policy Distillation: From VLA Generalists to RL Experts
2025 · 1 citations
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control
2019 · 1 citations
Rewarding DINO: Predicting Dense Rewards with Vision Foundation Models
2026
Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning
2021
Top co-authors
Joschka Boedecker
· 7
Jingwei Zhang
· 4
Lei Tai
· 3
Lukas Hermann
· 3
Ming Liu
· 3
Artemij Amiranashvili
· 2
Gabriel Kalweit
· 2
Max Argus
· 2
Nicolai Dorka
· 2
Oier Mees
· 2
Shengchao Yan
· 2
Thomas Brox
· 2
Topics
Model-Based RL
Meta-RL
Value-Based
Exploration
Offline RL
Policy Gradient
Safe RL
Multi-Agent