Optimal continuous state POMDP planning with semantic observations
Optimal continuous state POMDP planning with semantic observations
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具有语义观察的最优连续状态 POMDP 规划
DOI：
10.1109/cdc.2017.8263866
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
N. Ahmed
中科院分区：
文献类型：
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作者：
Luke Burks;N. Ahmed
这项工作开发了新的策略，使用连续状态部分可观测马尔可夫决策过程（CPOMDPs）的语义观测的最优规划。我们提出了两个主要的创新高斯混合（GM）CPOMDP政策近似方法。虽然这些最先进的方法有许多理论上很好的属性，但它们受到无法有效地表示和推理混合连续-离散概率模型的阻碍。第一个主要的创新是推导出封闭形式的变分贝叶斯（VB）GM近似的PBVI贝尔曼政策备份，使用softmax模型的连续离散语义观察概率。第二个主要的创新是一个新的聚类为基础的技术，混合冷凝，规模以及非常大的GM政策功能和信念功能。一个目标搜索和拦截任务与二进制语义观察的仿真结果表明，从这些创新所产生的GM政策比其他国家的最先进的GM近似产生的更有效，但需要显着更少的建模开销和运行时成本。
This work develops novel strategies for optimal planning with semantic observations using continuous state Partially Observable Markov Decision Processes (CPOMDPs). We propose two major innovations to Gaussian mixture (GM) CPOMDP policy approximation methods. While these state of the art methods have many theoretically nice properties, they are hampered by the inability to efficiently represent and reason over hybrid continuous-discrete probabilistic models. The first major innovation is the derivation of closed-form variational Bayes (VB) GM approximations of PBVI Bellman policy backups, using softmax models of continuous-discrete semantic observation probabilities. The second major innovation is a new clustering-based technique for mixture condensation that scales well to very large GM policy functions and belief functions. Simulation results for a target search and interception task with binary semantic observations show that the GM policies resulting from these innovations are more effective than those produced by other state of the art GM approximations, but require significantly less modeling overhead and runtime cost.