Predictive modeling of aircraft dynamics

An environment comprising:

• a host aircraft and
• a system configured to train a compressive encoder to direct next action of a the host aircraft, wherein the system comprises:
• a sensor system that comprises a sensor cone of the host aircraft configured to gather parameters that indicate a current state of each aircraft in a number of aircraft in flight in the sensor cone, wherein the current state comprises a velocity, respectively, of each of the number of aircraft;
• a storage device configured to store program instructions;
• and
• one or more processors that comprise a controller configured to determine the next action of the host aircraft and operably connected to the storage device and configured to execute the program instructions to cause the system to:
• calculate a difference between the current state, respectively, of each of the number of aircraft in the sensor cone and a previous state, respectively, of each of the number of aircraft in the sensor cone, wherein the current state comprises a reduced state in which a value of a specified parameter is missing;
• input the difference into compressive layers comprising an encoder of a neural network, wherein the compressive layers, each comprise respectively, a rectified linear unit activation
• generate by the compressive layers according to the difference, a learned value for the parameter missing from current state;
• input the current state into four fully connected hidden layers, each applying a second rectified linear unit activation, comprising a decoder of a neural network, in communication with a neuroevolution controller and the compressive layers;
• input the learned value into the controller and into the hidden layers concurrently with the current state;
• input, as one-hot encoding from control policies converted to a vector of values, an action applied to the aircraft into the hidden layers concurrently with the current state and learned value;
• determine, by the hidden layers according to the current state, learned value, and current action, a residual output that comprises an incremental difference in the current state of the aircraft resulting from the current action;
• calculate a ground truth residual consisting of the difference between the current state and a previous state of at least one of the aircraft;
• compute, a loss function based on the difference between the residual output and a ground truth residual;
• modify, based upon a gradient descent optimization, at least some neural network weights;
• generate, by the controller based upon the current state and the learned value, the next action;
• and
• execute, by the host aircraft, the next action.

A computer program product configured to train a compressive encoder wherein the computer program product comprise a controller configured to direct a next action of a host aircraft and a computer-readable storage medium that comprises program instructions configured to:

• receive, through a sensor system of the host aircraft, parameters of a number of aircraft in a sensor cone of the host aircraft that indicate a current state, respectively, for each of the number of aircraft in the sensor cone, wherein the current state comprises a velocity, respectively, of each of the number of aircraft;
• calculate a difference between the current state, respectively, of each of the number of aircraft in the sensor cone and a previous state, respectively, of each of the number of aircraft in the sensor cone, wherein the current state comprises a reduced state in which a value of the specified parameter is missing;
• input the difference into compressive layers comprising an encoder of a neural network, wherein the compressive layers, each comprise respectively, a rectified linear unit activation;
• generate, by the compressive layers according to the difference, a learned value for the specified parameter missing from the current state;
• input the current state into four fully connected hidden layers, each applying a second rectified linear unit activation, comprising a decoder of the neural network in communication with a neuroevolution controller and the compressive layers;
• input the learned value into a controller and into the hidden layers concurrently with the current state;
• input, as one-hot encoding from control policies converted to a vector of values, an action applied to the aircraft into the hidden layers concurrently with the current state and learned value;
• determine, based upon the hidden layers according to the current state, the learned value, and current action, a residual output that comprises an incremental difference in the current state of the aircraft resulting from the current action;
• calculate a ground truth residual that consists of the difference between the current state and a previous state of at least one of the aircraft;
• compute, a loss function based on the difference between the residual output and a ground truth residual;
• modify, based upon a gradient descent optimization, at least some neural network weights;
• generate, by the controller using the current state and the learned value, a the next action;
• and
• execute the next action by the host aircraft.