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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.15662 |
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| _version_ | 1866910839191109632 |
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| author | Hsiao, Vincent Roberts, Mark Hiatt, Laura M. Konidaris, George Nau, Dana |
| author_facet | Hsiao, Vincent Roberts, Mark Hiatt, Laura M. Konidaris, George Nau, Dana |
| contents | A major challenge for reinforcement learning is automatically generating curricula to reduce training time or improve performance in some target task. We introduce SEBNs (Skill-Environment Bayesian Networks) which model a probabilistic relationship between a set of skills, a set of goals that relate to the reward structure, and a set of environment features to predict policy performance on (possibly unseen) tasks. We develop an algorithm that uses the inferred estimates of agent success from SEBN to weigh the possible next tasks by expected improvement. We evaluate the benefit of the resulting curriculum on three environments: a discrete gridworld, continuous control, and simulated robotics. The results show that curricula constructed using SEBN frequently outperform other baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15662 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network Hsiao, Vincent Roberts, Mark Hiatt, Laura M. Konidaris, George Nau, Dana Artificial Intelligence Machine Learning A major challenge for reinforcement learning is automatically generating curricula to reduce training time or improve performance in some target task. We introduce SEBNs (Skill-Environment Bayesian Networks) which model a probabilistic relationship between a set of skills, a set of goals that relate to the reward structure, and a set of environment features to predict policy performance on (possibly unseen) tasks. We develop an algorithm that uses the inferred estimates of agent success from SEBN to weigh the possible next tasks by expected improvement. We evaluate the benefit of the resulting curriculum on three environments: a discrete gridworld, continuous control, and simulated robotics. The results show that curricula constructed using SEBN frequently outperform other baselines. |
| title | Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.15662 |