{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/behaviour-suite-for-reinforcement-learning","title":"Behaviour Suite for Reinforcement Learning","arxiv_id":"1908.03568","date":"2019-08-09","proceeding":"ICLR 2020 1","authors":["Ian Osband","Yotam Doron","Matteo Hessel","John Aslanides","Eren Sezener","Andre Saraiva","Katrina McKinney","Tor Lattimore","Csaba Szepesvari","Satinder Singh","Benjamin Van Roy","Richard Sutton","David Silver","Hado van Hasselt"],"abstract":"This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key issues in the design of general and efficient learning algorithms. Second, to study agent behaviour through their performance on these shared benchmarks. To complement this effort, we open source github.com/deepmind/bsuite, which automates evaluation and analysis of any agent on bsuite. This library facilitates reproducible and accessible research on the core issues in RL, and ultimately the design of superior learning algorithms. Our code is Python, and easy to use within existing projects. We include examples with OpenAI Baselines, Dopamine as well as new reference implementations. 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