C-procgen: Empowering Procgen With Controllable Contexts

Abstract

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keytan2023c

Related papers

• Leveraging Procedural Generation To Benchmark Reinforcement Learning (2019)0.00
• Measuring Sample Efficiency And Generalization In Reinforcement Learning Benchmarks: Neurips 2020 Procgen Benchmark (2021)0.00
• Improving Generalization On The Procgen Benchmark With Simple Architectural Changes And Scale (2024)0.00
• Contextualize Me -- The Case For Context In Reinforcement Learning (2022)0.00
• Contextual Intelligence The Next Leap For Reinforcement Learning (2026)0.00
• Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination (2025)0.00
• Griddly: A Platform For AI Research In Games (2020)0.00
• Environment Scaling For Interactive Agentic Experience Collection: A Survey (2025)0.00