Bamford, C;
Jiang, M;
Samvelyan, M;
Rocktäschel, T;
(2022)
GriddlyJS: A Web IDE for Reinforcement Learning.
In:
Advances in Neural Information Processing Systems.
NeurIPS
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Abstract
Progress in reinforcement learning (RL) research is often driven by the design of new, challenging environments-a costly undertaking requiring skills orthogonal to that of a typical machine learning researcher. The complexity of environment development has only increased with the rise of procedural-content generation (PCG) as the prevailing paradigm for producing varied environments capable of testing the robustness and generalization of RL agents. Moreover, existing environments often require complex build processes, making reproducing results difficult. To address these issues, we introduce GriddlyJS, a web-based Integrated Development Environment (IDE) based on the Griddly engine. GriddlyJS allows researchers to visually design and debug arbitrary, complex PCG grid-world environments using a convenient graphical interface, as well as visualize, evaluate, and record the performance of trained agent models. By connecting the RL workflow to the advanced functionality enabled by modern web standards, GriddlyJS allows publishing interactive agent-environment demos that reproduce experimental results directly to the web. To demonstrate the versatility of GriddlyJS, we use it to quickly develop a complex compositional puzzle-solving environment alongside arbitrary human-designed environment configurations and their solutions for use in automatic curriculum learning and offline RL. The GriddlyJS IDE is open source and freely available at https://griddly.ai.
Type: | Proceedings paper |
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Title: | GriddlyJS: A Web IDE for Reinforcement Learning |
Event: | 36th Conference on Neural Information Processing Systems (NeurIPS 2022) |
ISBN-13: | 9781713871088 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://proceedings.neurips.cc/paper_files/paper/2... |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10177323 |
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