AbideGym: Turning Static RL Worlds into Adaptive Challenges
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866916970329276416 |
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| author | Aryan, Abi Liu, Zac Childress, Aaron |
| author_facet | Aryan, Abi Liu, Zac Childress, Aaron |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21234 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AbideGym: Turning Static RL Worlds into Adaptive Challenges Aryan, Abi Liu, Zac Childress, Aaron Machine Learning Multiagent Systems 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. |
| title | AbideGym: Turning Static RL Worlds into Adaptive Challenges |
| topic | Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2509.21234 |