C-Procgen: Empowering Procgen with Controllable Contexts
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Abstract (arXiv)
We present C-Procgen, an enhanced suite of environments on top of the Procgen benchmark. C-Procgen provides access to over 200 unique game contexts across 16 games. It allows for detailed configuration of environments, ranging from game mechanics to agent attributes. This makes the procedural generation process, previously a black-box in Procgen, more transparent and adaptable for various research needs.The upgrade enhances dynamic context management and individualized assignments, while maintaining computational efficiency. C-Procgen's controllable contexts make it applicable in diverse reinforcement learning research areas, such as learning dynamics analysis, curriculum learning, and transfer learning. We believe that C-Procgen will fill a gap in the current literature and offer a valuable toolkit for future works.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 20 references that are in the map. First posted on arXiv 13 Nov 2023.
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19 of 20 references.
References · 19
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7 more
- 2020Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learningcited by 95
- 2019Observational Overfitting in Reinforcement Learningcited by 90
- 2018A Dissection of Overfitting and Generalization in Continuous Reinforcement Learningcited by 86
- 2020Improving Generalization in Reinforcement Learning with Mixture Regularizationcited by 81
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- 2021TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RLcited by 18
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