Web: http://arxiv.org/abs/2201.10003

Jan. 26, 2022, 2:11 a.m. | Charl Maree, Christian Omlin

cs.LG updates on arXiv.org arxiv.org

The increased complexity of state-of-the-art reinforcement learning (RL)
algorithms have resulted in an opacity that inhibits explainability and
understanding. This has led to the development of several post-hoc
explainability methods that aim to extract information from learned policies
thus aiding explainability. These methods rely on empirical observations of the
policy and thus aim to generalize a characterization of agents' behaviour. In
this study, we have instead developed a method to imbue a characteristic
behaviour into agents' policies through regularization of …

arxiv learning policy reinforcement learning

More from arxiv.org / cs.LG updates on arXiv.org

Director, Data Engineering and Architecture

@ Chainalysis | California | New York | Washington DC | Remote - USA

Deep Learning Researcher

@ Topaz Labs | Dallas, TX

Sr Data Engineer (Contractor)

@ SADA | US - West

Senior Cloud Database Administrator

@ Findhelp | Remote

Senior Data Analyst

@ System1 | Remote

Speech Machine Learning Research Engineer

@ Samsung Research America | Mountain View, CA