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arXiv · 2509.21234

AbideGym: Turning Static RL Worlds into Adaptive Challenges

Abi Aryan·Zac Liu·Aaron Childress
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Abstract

Agents trained with reinforcement learning often develop brittle policies that fail when dynamics shift, a problem amplified by static benchmarks. AbideGym, a dynamic MiniGrid wrapper, introduces agent-aware perturbations and scalable complexity to enforce intra-episode adaptation. By exposing weaknesses in static policies and promoting resilience, AbideGym provides a modular, reproducible evaluation framework for advancing research in curriculum learning, continual learning, and robust generalization.

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Abi Aryan, Zac Liu, Aaron Childress. 2025-09-25. AbideGym: Turning Static RL Worlds into Adaptive Challenges. https://arxiv.org/abs/2509.21234

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