Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866910772688322560 |
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| author | Wakayama, Shohei Ahmed, Nisar |
| author_facet | Wakayama, Shohei Ahmed, Nisar |
| contents | We introduce a new variant of contextual multi-armed bandits (CMABs) called observation-augmented CMABs (OA-CMABs) wherein a robot uses extra outcome observations from an external information source, e.g. humans. In OA-CMABs, external observations are a function of context features and thus provide evidence on top of observed option outcomes to infer hidden parameters. However, if external data is error-prone, measures must be taken to preserve the correctness of inference. To this end, we derive a robust Bayesian inference process for OA-CMABs based on recently developed probabilistic semantic data association techniques, which handle complex mixture model parameter priors and hybrid discrete-continuous observation likelihoods for semantic external data sources. To cope with combined uncertainties in OA-CMABs, we also derive a new active inference algorithm for optimal option selection based on approximate expected free energy minimization. This generalizes prior work on CMAB active inference by accounting for faulty observations and non-Gaussian distributions. Results for a simulated deep space search site selection problem show that, even if incorrect semantic observations are provided externally, e.g. by scientists, efficient decision-making and robust parameter inference are still achieved in a wide variety of conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_12583 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration Wakayama, Shohei Ahmed, Nisar Robotics Machine Learning We introduce a new variant of contextual multi-armed bandits (CMABs) called observation-augmented CMABs (OA-CMABs) wherein a robot uses extra outcome observations from an external information source, e.g. humans. In OA-CMABs, external observations are a function of context features and thus provide evidence on top of observed option outcomes to infer hidden parameters. However, if external data is error-prone, measures must be taken to preserve the correctness of inference. To this end, we derive a robust Bayesian inference process for OA-CMABs based on recently developed probabilistic semantic data association techniques, which handle complex mixture model parameter priors and hybrid discrete-continuous observation likelihoods for semantic external data sources. To cope with combined uncertainties in OA-CMABs, we also derive a new active inference algorithm for optimal option selection based on approximate expected free energy minimization. This generalizes prior work on CMAB active inference by accounting for faulty observations and non-Gaussian distributions. Results for a simulated deep space search site selection problem show that, even if incorrect semantic observations are provided externally, e.g. by scientists, efficient decision-making and robust parameter inference are still achieved in a wide variety of conditions. |
| title | Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2312.12583 |