A system includes a machine learning engine configured to receive training data including a plurality of input conditions associated with a state space and a plurality of response maneuvers associated with the state space and train a learning system using the training data and a reward function including a plurality of terms associated with a plurality of end state spaces, each term in the plurality of terms defines an end reward value for each end state space. A value function and policy are generated. The value function comprising a plurality of values, wherein each response maneuvers in the plurality of response maneuvers is associated with a value in the plurality of values related to transitioning from the state space to each end state space, the policy indicative of connections between the state spaces, plurality of values, and the respective end reward value for the plurality of end state spaces.


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    Title :

    Systems and methods for practical autonomy decision controller


    Contributors:

    Publication date :

    2021-08-31


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / B64C AEROPLANES , Flugzeuge / G05D SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES , Systeme zum Steuern oder Regeln nichtelektrischer veränderlicher Größen



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