The technology relates to navigating aerial vehicles using deep reinforcement learning techniques to generate flight policies. An operational system for controlling flight of an aerial vehicle may include a computing system configured to process an input vector representing a state of the aerial vehicle and output an action, an operation-ready policies server configured to store a trained neural network encoding a learned flight policy, and a controller configured to control the aerial vehicle. The input vector may be processed using the trained neural network encoding the learned flight policy. A method for navigating an aerial vehicle may include selecting a trained neural network encoding a learned flight policy from an operation policies server, generating an input vector comprising a set of characteristics representing a state of the aerial vehicle, selecting an action, by the trained neural network, based on the input vector, converting the action into a set of commands, by a flight computer, the set of commands configured to cause the aerial vehicle to perform the action, and causing, by a controller, the aerial vehicle to perform the action using the set of commands.


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    Title :

    Systems and Methods for Navigating Aerial Vehicles Using Deep Reinforcement Learning


    Contributors:

    Publication date :

    2021-04-29


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G05D SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES , Systeme zum Steuern oder Regeln nichtelektrischer veränderlicher Größen / B64B LIGHTER-THAN-AIR AIRCRAFT , Luftfahrzeuge leichter als Luft / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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