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Poster
TempoRL: Learning When to Act
André Biedenkapp · Raghu Rajan · Frank Hutter · Marius Lindauer

Wed Jul 21 09:00 AM -- 11:00 AM (PDT) @ Virtual
in Poster Session 3 »

Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment. However, behaviours are usually learned in a purely reactive fashion, where an appropriate action is selected based on an observation. In this form, it is challenging to learn when it is necessary to execute new decisions. This makes learning inefficient especially in environments that need various degrees of fine and coarse control. To address this, we propose a proactive setting in which the agent not only selects an action in a state but also for how long to commit to that action. Our TempoRL approach introduces skip connections between states and learns a skip-policy for repeating the same action along these skips. We demonstrate the effectiveness of TempoRL on a variety of traditional and deep RL environments, showing that our approach is capable of learning successful policies up to an order of magnitude faster than vanilla Q-learning.

Keywords: Non-convex Optimization · Reinforcement Learning and Planning · Neuroscience and Cognitive Science -> Reasoning; Optimization · Combinatorial Optimization; Reinforcement Learning and Plannin
[ Slides] 

Author Information

André Biedenkapp (University of Freiburg)

Since October 2017 I am a PhD student at the Machine Learning Group under the supervision of Frank Hutter and Marius Lindauer. Before that I completed my master and bachelor degrees in computer science at the University of Freiburg. Research Interests I am interested in all facets of artificial intelligence. My research focuses on new ways to control the behavior of algorithms online. More precisely my research areas include: Dynamic Algorithm Configuration/Algorithm Control Learning to Learn (Deep) Reinforcement Learning Bayesian Optimization Automated Hyperparameter Optimization

Raghu Rajan (University of Freiburg)
Frank Hutter (University of Freiburg and Bosch Center for Artificial Intelligence)
Frank Hutter

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), where he has been a faculty member since 2013. Before that, he was at the University of British Columbia (UBC) for eight years, for his PhD and postdoc. Frank's main research interests lie in machine learning, artificial intelligence and automated algorithm design. For his 2009 PhD thesis on algorithm configuration, he received the CAIAC doctoral dissertation award for the best thesis in AI in Canada that year, and with his coauthors, he received several best paper awards and prizes in international competitions on automated machine learning, SAT solving, and AI planning. Since 2016 he holds an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning.

Marius Lindauer (Leibniz University Hannover)

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