Markov decision processes with unknown state feature values for safe exploration using Gaussian processes
When exploring an unknown environment, a mobile robot must decide where to observe next. It must do this whilst minimising the risk of failure, by only exploring areas that it expects to be safe. In this context, safety refers to the robot remaining in regions where critical environment features (e....
المؤلفون الرئيسيون: | Budd, M, Lacerda, B, Duckworth, P, West, A, Lennox, B, Hawes, N |
---|---|
التنسيق: | Conference item |
اللغة: | English |
منشور في: |
Institute of Electrical and Electronics Engineers
2021
|
مواد مشابهة
-
Planning under uncertainty for safe robot exploration using Gaussian process prediction
حسب: Stephens, A, وآخرون
منشور في: (2024) -
On solving a Stochastic Shortest-Path Markov Decision Process as probabilistic inference
حسب: Baioumy, M, وآخرون
منشور في: (2022) -
Bayesian reinforcement learning for single-episode missions in partially unknown environments
حسب: Budd, M, وآخرون
منشور في: (2022) -
Time-bounded mission planning in time-varying domains with semi-MDPS and Gaussian processes
حسب: Duckworth, P, وآخرون
منشور في: (2021) -
Minimax regret optimisation for robust planning in uncertain Markov decision processes
حسب: Rigter, M, وآخرون
منشور في: (2021)