A Continual Offline Reinforcement Learning Benchmark for Navigation Tasks
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908799345885184 |
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| author | Kobanda, Anthony Maillard, Odalric-Ambrym Portelas, Rémy |
| author_facet | Kobanda, Anthony Maillard, Odalric-Ambrym Portelas, Rémy |
| contents | Autonomous agents operating in domains such as robotics or video game simulations must adapt to changing tasks without forgetting about the previous ones. This process called Continual Reinforcement Learning poses non-trivial difficulties, from preventing catastrophic forgetting to ensuring the scalability of the approaches considered. Building on recent advances, we introduce a benchmark providing a suite of video-game navigation scenarios, thus filling a gap in the literature and capturing key challenges : catastrophic forgetting, task adaptation, and memory efficiency. We define a set of various tasks and datasets, evaluation protocols, and metrics to assess the performance of algorithms, including state-of-the-art baselines. Our benchmark is designed not only to foster reproducible research and to accelerate progress in continual reinforcement learning for gaming, but also to provide a reproducible framework for production pipelines -- helping practitioners to identify and to apply effective approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02883 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Continual Offline Reinforcement Learning Benchmark for Navigation Tasks Kobanda, Anthony Maillard, Odalric-Ambrym Portelas, Rémy Machine Learning Autonomous agents operating in domains such as robotics or video game simulations must adapt to changing tasks without forgetting about the previous ones. This process called Continual Reinforcement Learning poses non-trivial difficulties, from preventing catastrophic forgetting to ensuring the scalability of the approaches considered. Building on recent advances, we introduce a benchmark providing a suite of video-game navigation scenarios, thus filling a gap in the literature and capturing key challenges : catastrophic forgetting, task adaptation, and memory efficiency. We define a set of various tasks and datasets, evaluation protocols, and metrics to assess the performance of algorithms, including state-of-the-art baselines. Our benchmark is designed not only to foster reproducible research and to accelerate progress in continual reinforcement learning for gaming, but also to provide a reproducible framework for production pipelines -- helping practitioners to identify and to apply effective approaches. |
| title | A Continual Offline Reinforcement Learning Benchmark for Navigation Tasks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.02883 |