Utilizing Priors in Sampling-based Cost Minimization
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909329344430080 |
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| author | Lou, Yuan-Yao Spencer, Jonathan Kim, Kwang Taik Chiang, Mung |
| author_facet | Lou, Yuan-Yao Spencer, Jonathan Kim, Kwang Taik Chiang, Mung |
| contents | We consider an autonomous vehicle (AV) agent performing a long-term cost-minimization problem in the elapsed time $T$ over sequences of states $s_{1:T}$ and actions $a_{1:T}$ for some fixed, known (though potentially learned) cost function $C(s_t,a_t)$, approximate system dynamics $P$, and distribution over initial states $d_0$. The goal is to minimize the expected cost-to-go of the driving trajectory $τ= s_1, a_1, ..., s_T, a_T$ from the initial state. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19834 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Utilizing Priors in Sampling-based Cost Minimization Lou, Yuan-Yao Spencer, Jonathan Kim, Kwang Taik Chiang, Mung Systems and Control We consider an autonomous vehicle (AV) agent performing a long-term cost-minimization problem in the elapsed time $T$ over sequences of states $s_{1:T}$ and actions $a_{1:T}$ for some fixed, known (though potentially learned) cost function $C(s_t,a_t)$, approximate system dynamics $P$, and distribution over initial states $d_0$. The goal is to minimize the expected cost-to-go of the driving trajectory $τ= s_1, a_1, ..., s_T, a_T$ from the initial state. |
| title | Utilizing Priors in Sampling-based Cost Minimization |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2409.19834 |