Sabrina Hoppe — accepted AI-conference papers

2020

Sample-Efficient Learning for Industrial Assembly using Qgraph-bounded DDPG

Sabrina Hoppe, Markus Giftthaler, Robert Krug, Marc Toussaint

IROS 2020poster

Recent progress in deep reinforcement learning has enabled agents to autonomously learn complex control strategies from scratch. Model-free approaches like Deep Deterministic Policy Gradients (DDPG) seem promising for applications with intricate dynamics, such as contact-rich manipulation tasks. How…

Cited by 13 Source ↗Scholar ↗

2019

Planning Approximate Exploration Trajectories for Model-Free Reinforcement Learning in Contact-Rich Manipulation

Sabrina Hoppe, Zhongyu Lou, Daniel Hennes, Marc Toussaint

RA-L 2019

Recent progress in deep reinforcement learning has enabled simulated agents to learn complex behavior policies from scratch, but their data complexity often prohibits real-world applications. The learning process can be sped up by expert demonstrations but those can be costly to acquire. We demonstr

Cited by 25 Source ↗Scholar ↗