mlsanityData to 7 Oct 2026

GriddlyJS: A Web IDE for Reinforcement Learning

arXiv 2207.06105 ↗First posted 13 Jul 2022cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 42 of its 77 references that are in the map. First posted on arXiv 13 Jul 2022.

Builds on

42 of 77 references.

References · 42

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  2. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,406
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30 more
  1. 2020Prioritized Level Replaycited by 190
  2. 2018A Study on Overfitting in Deep Reinforcement Learningcited by 170
  3. 2022Evolving Curricula with Regret-Based Environment Designcited by 166
  4. 2017Procedural Content Generation via Machine Learning (PCGML)cited by 136
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  6. 2020Phasic Policy Gradientcited by 121
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  8. 2019Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environmentscited by 113
  9. 2021Replay-Guided Adversarial Environment Designcited by 108
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  19. 2020Align-RUDDER: Learning From Few Demonstrations by Reward Redistributioncited by 35
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  22. 2019TensorFlow.js: Machine Learning for the Web and Beyondcited by 29
  23. 2019Working Memory Graphscited by 23
  24. 2022Insights From the NeurIPS 2021 NetHack Challengecited by 20
  25. 2021TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RLcited by 18
  26. 2022Path of Destruction: Learning an Iterative Level Generator Using a Small Datasetcited by 14
  27. 2022Local Feature Swapping for Generalization in Reinforcement Learningcited by 9
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  30. 2021VisualEnv: visual Gym environments with Blendercited by 1

Built on by

Cited by year

Citing papers per year: 2023: 4.

4 papers.

  1. 2023MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learningcited by 30
  2. 2023Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challengescited by 2
  3. 2023Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Gamescited by 1
  4. 2023Selective imitation on the basis of reward function similaritycited by 0

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