Inverse Optimal Control (IOC) deals with the problem of recovering an unknown
cost function in a Markov decision process from expert demonstrations acting optimally.
In this thesis we apply IOC to SARS-CoV-2 data. For our application we use
the (mutated) sequences found in SARS-CoV-2 data as the expert demonstrations.
We present a way to learn useful state representations for this data, and successfully
apply IOC on a special class of Markov decision processes which allow for an
efficient computation ...
Inverse Optimal Control (IOC) deals with the problem of recovering an unknown
cost function in a Markov decision process from expert demonstrations acting optimally.
In this thesis we apply IOC to SARS-CoV-2 data. For our application we use
the (mutated) sequences found in SARS-CoV-2 data as the expert demonstrations.
We present a way to learn useful state representations for this data, and successfully
apply IOC on a special class of Markov decision processes which allow for an
efficient computation of the value and cost functions of the states, and informative
2D representations of the state.
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