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cs.LG2021
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…
cs.LG2021
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…