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[R] Building a Better CartPole – DM’s new RL benchmarking suite

Behaviour Suite for Reinforcement Learning

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 this http URL, 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. Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of bsuite from a committee of prominent researchers.

This is a great paper. While the authors focus on comparing different agents, additional value is going to be in debugging algorithm variants. Many researchers already have their own zoos of duct-taped diagnostic envs to try and localise errors, but the community’s been lacking anything ready-made and well-tested.

What is a little disappointing is that they don’t carry this paper through to ‘here we evaluated 17 different agents and this is the best one’, though presumably other contributors will fix that in short order.

submitted by /u/andyljones
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.