Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. The system includes a communication interface, a processor, memory, and software code stored in the memory. The software code, when executed, causes the system to: instantiate an automated agent for communicating resource task requests; receive a current feature data structure related to a resource of the resource task requests; maintain a plurality of historical feature data structures related to said resource for a plurality of prior time steps; compute normalized feature data using the current feature data structure and the plurality of historical feature data structures; compute supplemented state data appended with the normalized feature data; and transmit said supplemented state data to the reinforcement learning neural network to train said automated agent.

Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to select options for decisions in connection with design features of a computing device. In a particular implementation, design options for two or more design decisions of neural network processing device may be selected based, at least in part, on combination of function values that are computed based, at least in part, on a tensor expressing sample neural network weights.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for few-shot learning-based generator training based on raw data collected from a specific domain or class. In cases where the raw data is collected from multiple domains but is not easily divisible into classes, the invention describes training multiple generators based on a pivot-sample-based training process. Pivot samples are randomly selected from the raw data for clustering, and each cluster of raw data may be used to train a generator using the few-shot learning-based training process.

A non-transitory computer readable storage medium store one or more programs including instructions causing an electronic device to receive, while a visual object is in a first state among a plurality of states, a user input for switching a state of the visual object to a second state among the plurality of states; provide data regarding the user input as input data to a neural network for training of the neural network; identify a third state among the plurality of states, wherein the third state is an intermediate state for switching the first state to the second state; obtain, from the neural network, data regarding the third state as output data for the data regarding the user input; determine a compensation value for the data regarding the third state; and train, by providing the data regarding the compensation value to the neural network, the neural network.

The invention discloses a method to support the trajectory planning considering both the flight trajectory and the visual images (VI) from air traffic control (ATC) system for air traffic controllers (ATCOs) (VI from ATC system for ATCOs), comprising the following steps: Step 1: acquire the VI and the flight trajectory to serve as the method inputs, and extract features of the VI and the relative position of the aircraft; Step 2: construct reinforcement learning-based methods to support the decision-making for flight path planning and conduct the training procedures of the models in the proposed method; Step 3: based on the optimized reinforcement learning-based methods, predict the required operation sequence to guide the flight to the target waypoint. The method of the invention can support the flight path planning for air traffic operation in a safe and efficient manner and is able to reduce the workload of air traffic controllers.

The invention discloses a method to support the trajectory planning considering both the flight trajectory and the visual images (VI) from air traffic control (ATC) system for air traffic controllers (ATCOs) (VI from ATC system for ATCOs), comprising the following steps: Step 1: acquire the VI and the flight trajectory to serve as the method inputs, and extract features of the VI and the relative position of the aircraft; Step 2: construct reinforcement learning-based methods to support the decision-making for flight path planning and conduct the training procedures of the models in the proposed method; Step 3: based on the optimized reinforcement learning-based methods, predict the required operation sequence to guide the flight to the target waypoint. The method of the invention can support the flight path planning for air traffic operation in a safe and efficient manner and is able to reduce the workload of air traffic controllers.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

Provided are a reinforcement learning-based label-free six-dimensional object pose prediction method and apparatus. The method includes: obtaining a target image to be predicted, the target image being a two-dimensional image including a target object; performing pose prediction based on the target image by using a pre-trained pose prediction model to obtain a prediction result, the pose prediction model being obtained by performing reinforcement learning based on a sample image; and determining a three-dimensional position and a three-dimensional direction of the target object based on the prediction result. The pose prediction model is trained by introducing reinforcement learning, the pose prediction is performed based on the target image by using the pre-trained pose prediction model, and thus the problem of six-dimensional object pose estimation based on two-dimensional images can be solved in the absence of real pose annotation, which ensures the prediction effect of label-free six-dimensional object pose prediction.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adjusting a target neural network using automatically generated test cases before deployment of the target neural network in a deployment environment. One of the methods may include generating a plurality of test inputs by using a test case generation neural network; processing the plurality of test inputs using a target neural network to generate one or more test outputs for each test input; and identifying, from the one or more test outputs generated by the target neural network for each test input, failing test inputs that result in generation of test outputs by the target neural network that fail one or more criteria.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adjusting a target neural network using automatically generated test cases before deployment of the target neural network in a deployment environment. One of the methods may include generating a plurality of test inputs by using a test case generation neural network; processing the plurality of test inputs using a target neural network to generate one or more test outputs for each test input; and identifying, from the one or more test outputs generated by the target neural network for each test input, failing test inputs that result in generation of test outputs by the target neural network that fail one or more criteria.