[2011.09294] Using Unity to Help Solve Intelligenceopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

In the pursuit of artificial general intelligence, our most significant measurement of progress is an agent's ability to achieve goals in a wide range of environments. Existing platforms for constructing such environments are typically constrained by the technologies they are founded on, and are therefore only able to provide a subset of scenarios necessary to evaluate progress. To overcome these shortcomings, we present our use of Unity, a widely recognized and comprehensive game engine, to create more diverse, complex, virtual simulations. We describe the concepts and components developed to simplify the authoring of these environments, intended for use predominantly in the field of reinforcement learning. We also introduce a practical approach to packaging and re-distributing environments in a way that attempts to improve the robustness and reproducibility of experiment results. To illustrate the versatility of our use of Unity compared to other solutions, we highlight environments

2 mentions: @DeepMind
Date: 2020/11/19 18:52

Referring Tweets

@DeepMind Work by Tom Ward, Andrew Bolt, @nikhemmings, Simon Carter, Manuel Sanchez, Ricardo Barreira, @sebnoury, Keith Anderson, Jay Lemmon, Jonathan Coe @piotrtrochim, Tom Handley & @adrianbolton. Read more: t.co/A0MIhDYNpV

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