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Note, there is an extended version of that paper: https://digital-library.theiet.org/content/journals/10.1049/iet-cps.2019.0045 As you note, the best way to implement that algorithm is to define a belief-space generative MDP and use MCTS with DPW (e.g. with the implementation here https://github.com/JuliaPOMDP/MCTS.jl or your own) to solve it. I believe this is the code for that project: https://github.com/pslade2/SimulEstControl, but it is very out-of-date at this point, and it is probably easier to do what I described above.
This is probably possible, but there are probably rough edges like the ones you are encountering in #513 .
Nope, it is the same except for the belief update technique |
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I am currently getting used to the different MCTS variants.
i was curious if there is any implementation of the paper https://arxiv.org/pdf/1707.09055.pdf. I would love to guide my self and try an implementation with quickpomdp Python interface.
For me the basic algorithm seems like a combination of classic MCTS DPW with a generative model based on an EKF update.
as far as I understood, we sample a possible observation and perform an update step which defines the generative model of the mcts.
additionally I found the PTF-DPW algorithm in https://arxiv.org/pdf/1709.06196.pdf and was curious if there is any further difference between the EKF based and this beside the kalman filter technique.
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