Abstract: Systems, computer program products, and methods are described for advanced congestion control using multiple congestion indicators in a networking environment. An example system may include an intelligent agent configured to learn congestion control policies. The agent may interact with real-world or simulated environments replicating real-world benchmarks. Congestion indicators such as telemetry information, packet drop metrics, congestion notification packet rate, pause frame rate, port utilization metrics, and/or the like form a comprehensive state representation of the network, enabling congestion state of the network environment. The intelligent agent evaluates these conditions using a reward function to optimize network performance. The intelligent agent may then implement a behavioral policy in response to the captured congestion indicators, thereby changing the congestion state of the network environment.

Abstract: A conditional adversarial latent model (CALM) process can be used to generate reference motions from a set of original reference movements to create a library of new movements for an agent. The agent can be a virtual representation various types of characters, animals, or objects. The CALM process can receive a set of reference movements and a requested movement. An encoder can be used to map the requested movement onto a latent space. A low-level policy can be employed to produce a series of latent space joint movements for the agent. A conditional discriminator can be used to provide feedback to the low-level policy to produce stationary distributions over the states of the agent. A high-level policy can be employed to provide a macro movement control over the low-level policy movements, such as providing direction in the environment. The high-level policy can utilize a reward or a finite-state machine function.

Abstract: Reinforcement learning, which is a machine learning technique where a model learns to make decisions that maximize a reward, has shown great promise in various domains that involve sequential decision making, including for many real-world tasks, such as inventory management, traffic signal optimization, network optimization, resource allocation, and robotics. However, current neural network (NN) based solutions for reinforcement learning struggle with interpretability, handling categorical data, and supporting light implementations suitable for low-compute devices. The present disclosure provides a gradient boosting trees (GBT) framework that is tailored for reinforcement learning, which may enable interpretability, may be well suited for real-world tasks with structured data, and may be capable of deployment on low-compute devices.

Abstract: One embodiment of a method for animating characters includes receiving one or more goals specified in one or more modalities, generating, via a trained machine learning model and based on the one or more goals, a first action for a character to perform, where the trained machine learning model is trained to process inputs in multiple modalities, and causing the character to perform the first action within a computer-based or physical environment.

Abstract: One embodiment of a method for animating characters includes receiving one or more goals specified in one or more modalities, generating, via a trained machine learning model and based on the one or more goals, a first action for a character to perform, where the trained machine learning model is trained to process inputs in multiple modalities, and causing the character to perform the first action within a computer-based or physical environment.

Abstract: Methods, systems, devices, and computer program products for machine learning in datacenter applications are provided. An example method includes receiving, by a centralized computing device, data packets from a networked device communicably coupled with the centralized computing device. The networked device is associated with performance of at least a first machine learning based task, and each of the data packets include data entries generated by the networked device based on data traffic associated with the at least one networked device and/or one or more modifications thereto. The method further includes generating updated operational parameters associated with the first machine learning based task based on the data entries forming the plurality of data packets where the updated operational parameters are generated locally by the centralized computing device. The method also includes transmitting, by the centralized computing device, the updated operational parameters to the networked device.

Abstract: Systems and devices for network data collection and processing are provided. An example system includes a first networked device and a centralized computing device communicably coupled with the at least one networked device. The first networked device operates to generate event-driven data entries associated with the first networked device and generate first data packets including the event-driven data entries and/or manipulated outputs generated based on manipulations to the event-driven data entries. The centralized computing device receives the first data packets from the first networked device and determines configuration updates based on the first data packets. The configuration updates are generated locally by the centralized computing device, and the centralized computing device transmits the one or more configuration updates to the first networked device.

Abstract: Text-to-image generation generally refers to the process of generating an image from one or more text prompts input by a user and in some cases also a user provided sample image. Existing text-to-image generation processes are configured to only generate content from text and usually non-original sample images (e.g. obtained from the Internet). This limits the customization options available to the user. The present disclosure provides a sketch-to-3D content generation process which allows users to generate 3D content from a given 3D human generated, or free-form, sketch, which enables greater customization of computer generated 3D content.

Abstract: One embodiment of a method for controlling a character includes receiving a state of the character, a path to follow, and first information about a scene, generating, via a trained machine learning model and based on the state of the character, the path, and the first information, a first action for the character to perform, wherein the first action comprises a first type of motion included in a plurality of types of motions for which the trained machine learning model is trained to generate actions, and causing the character to perform the first action.

Abstract: One embodiment of a method for controlling a character includes receiving a state of the character, a path to follow, and first information about a scene, generating, via a trained machine learning model and based on the state of the character, the path, and the first information, a first action for the character to perform, wherein the first action comprises a first type of motion included in a plurality of types of motions for which the trained machine learning model is trained to generate actions, and causing the character to perform the first action.

Abstract: In various examples, systems and methods are disclosed relating to receive at least one of a text prompt or a kinematic constraint and determine first human motion data using a motion model by applying the at least one of the text prompt or the kinematic constraint to the motion model. The motion model is updated by generating, using the motion model, second human motion data by applying motion capture (mocap) data and video reconstruction data as inputs to the motion model, receiving user feedback information for the second human motion data, and updating the motion model based on the user feedback information. The video reconstruction data is generated by reconstructing human motions from a plurality of videos. Physically implausible artifacts are filtered from the video reconstruction data using a motion imitation controller. The motion imitation controller is updated using at least one of Reinforced Learning (RL) or physics-based character simulations.

Abstract: Systems and methods are disclosed relating to receiving at least one of a text prompt or a kinematic constraint, generating, by a motion model including a first model and a second model, human motion data of a human character by applying a random noise and the at least one of the text prompt or the kinematic constraint into the motion model. Generating the human motion data includes, for each iteration of diffusion determining, using the first model, global root motion by applying noisy global root motion and noisy local joint motion as inputs into the first model and determining, using the second model, local joint motion by applying the noisy local joint motion and local root motion as inputs into the second model. The local root motion is determined based on the global root motion. The human motion data includes the local joint motion and the global root motion.

Abstract: One embodiment of a method for animating characters includes receiving a first state of a character and one or more constraints on one or more motions associated with a subset of joints belonging to the character, generating, via a trained machine learning model and based on the first state and the one or more constraints, a first action for the character to perform, and causing the character to perform the first action within a computer-based or physical environment.

Abstract: A conditional adversarial latent model (CALM) process can be used to generate reference motions from a set of original reference movements to create a library of new movements for an agent. The agent can be a virtual representation various types of characters, animals, or objects. The CALM process can receive a set of reference movements and a requested movement. An encoder can be used to map the requested movement onto a latent space. A low-level policy can be employed to produce a series of latent space joint movements for the agent. A conditional discriminator can be used to provide feedback to the low-level policy to produce stationary distributions over the states of the agent. A high-level policy can be employed to provide a macro movement control over the low-level policy movements, such as providing direction in the environment. The high-level policy can utilize a reward or a finite-state machine function.

Abstract: In various embodiments, a congestion control modelling application automatically controls congestion in data transmission networks. The congestion control modelling application executes a trained neural network in conjunction with a simulated data transmission network to generate a training dataset. The trained neural network has been trained to control congestion in the simulated data transmission network. The congestion control modelling application generates a first trained decision tree model based on an initial loss for an initial model relative to the training dataset. The congestion control modelling application generates a final tree-based model based on the first trained decision tree model and at least a second trained decision tree model. The congestion control modelling application executes the final tree-based model in conjunction with a data transmission network to control congestion within the data transmission network.

Abstract: A reinforcement learning agent learns a congestion control policy using a deep neural network and a distributed training component. The training component enables the agent to interact with a vast set of environments in parallel. These environments simulate real world benchmarks and real hardware. During a learning process, the agent learns how maximize an objective function. A simulator may enable parallel interaction with various scenarios. As the trained agent encounters a diverse set of problems it is more likely to generalize well to new and unseen environments. In addition, an operating point can be selected during training which may enable configuration of the required behavior of the agent.

Abstract: A reinforcement learning agent learns a congestion control policy using a deep neural network and a distributed training component. The training component enables the agent to interact with a vast set of environments in parallel. These environments simulate real world benchmarks and real hardware. During a learning process, the agent learns how maximize an objective function. A simulator may enable parallel interaction with various scenarios. As the trained agent encounters a diverse set of problems it is more likely to generalize well to new and unseen environments. In addition, an operating point can be selected during training which may enable configuration of the required behavior of the agent.