5 papers
CIG: Exploration via Conditional Information Gain
Tim Joseph, Marcus Fechner, Philipp Stegmaier +2
Intrinsic rewards for exploration in reinforcement learning condition on different contexts: lifelong rewards score each transition against accumulated experience but ignore within…
Constrained Meta Agnostic Reinforcement Learning
Karam Daaboul, Florian Kuhm, Tim Joseph +1
Meta-Reinforcement Learning (Meta-RL) aims to acquire meta-knowledge for quick adaptation to diverse tasks. However, applying these policies in real-world environments presents a s…
Robotic Control Using Model Based Meta Adaption
Karam Daaboul, Joel Ikels, Marius Zöllner
In machine learning, meta-learning methods aim for fast adaptability to unknown tasks using prior knowledge. Model-based meta-reinforcement learning combines reinforcement learning…
Safe Continuous Control with Constrained Model-Based Policy Optimization
Moritz A. Zanger, Karam Daaboul, J. Marius Zöllner
The applicability of reinforcement learning (RL) algorithms in real-world domains often requires adherence to safety constraints, a need difficult to address given the asymptotic n…
Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning
Karl Kurzer, Philip Schörner, Alexander Albers +3
Data driven approaches for decision making applied to automated driving require appropriate generalization strategies, to ensure applicability to the world's variability. Current a…