Huawei Technologies Co., Limited

Jun Luo

Mohsen Rohani

Peyman Yadmellat

Elmira Amirloo Abolfathi

Yingying Fu

Seyed Ershad Banijamali

Zichen Zhang

Julian Villella

Khurram Javed

Cameron Goeffrey Watmough Haigh

Negar Hassanpour

Systems and methods for computer-assisted design of an inductor are described. Target specifications for an inductor are received. An inductor design is generated segment-by-segment using a reinforcement learning agent to generate segment parameters for each added segment. The reinforcement learning agent implements a policy that is learned using a reward computed based on performance of the generated inductor design relative to the target specifications. The generated inductor design is outputted as a candidate inductor design after determining that the generated inductor design satisfies a predefined performance threshold.

System and method for computer-assisted design of inductor for voltaged-controlled oscillator

Methods and systems of training RL agent for autonomous operation of a vehicle are described. The RL agent is trained using uniformly sampled training samples and learning a policy. After the RL agent has achieved a predetermined performance goal, data is collected including a sequence of sampled states, and for each sequence of sampled states, agent parameters, and an indication of failure of the RL agent for the sequence. A failure predictor is trained, using samples from the collected data, to predict a probability of failure of the RL agent for a given sequence of states. Sequences of states are collected by simulating interaction of the vehicle with the environment. Based on a probability of failure outputted by the failure predictor, a sequence of states is selected. The RL agent is further trained based on the selected sequence of states.

Method and system for training reinforcement learning agent using adversarial sampling

Growing community of inventors

Toronto, Canada

Jun Luo