公开(公告)号：US20240249458A1

公开(公告)日：2024-07-25

申请号：US18364982

申请日：2023-08-03

Applicant: NVIDIA Corporation

Inventor： Chen Tessler ,  Gal Chechik ,  Yoni Kasten ,  Shie Mannor ,  Jason Peng

IPC: G06T13/40 ,  G06N3/08 ,  G06T13/80

CPC classification number: G06T13/40 ,  G06N3/08 ,  G06T13/80

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.

公开(公告)号：US20230041242A1

公开(公告)日：2023-02-09

申请号：US17959042

申请日：2022-10-03

Applicant: NVIDIA Corporation

Inventor： Shie Mannor ,  Chen Tessler ,  Yuval Shpigelman ,  Amit Mandelbaum ,  Gal Dalal ,  Doron Kazakov ,  Benjamin Fuhrer

IPC: H04L43/0817 ,  H04L43/067 ,  H04L43/0852 ,  G06N3/08 ,  H04L47/122 ,  G06K9/62 ,  H04L43/0882

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.

公开(公告)号：US20220231933A1

公开(公告)日：2022-07-21

申请号：US17341210

申请日：2021-06-07

Applicant: NVIDIA Corporation

Inventor： Shie Mannor ,  Chen Tessler ,  Yuval Shpigelman ,  Amit Mandelbaum ,  Gal Dalal ,  Doron Kazakov ,  Benjamin Fuhrer

IPC: H04L12/26 ,  H04L12/803 ,  G06K9/62 ,  G06N3/08

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.