Tianyi Chen

20230113168, Apr 13, 2023

Oct 12, 2021

17/499815

- Armonk NY, US
- Tory NY, US
Sijia Liu - Somerville MA, US
Tianyi Chen - Rensselaer NY, US

G06N 20/00
G05B 13/04
B60W 50/00

A reinforcement learning system includes a plurality of agents, each agent having an individual reward function and one or more safety constraints that involve joint actions of the agents, wherein each agent maximizes a team-average long-term return in performing the joint actions, subject to the safety constraints, and participates in operating a physical system. A peer-to-peer communication network is configured to connect the plurality of agents. A distributed constrained Markov decision process (D-CMDP) model is implemented over the peer-to-peer communication network and is configured to perform policy optimization using a decentralized policy gradient (PG) method, wherein the participation of each agent in operating the physical system is based on the D-CMDP model.

20200302163, Sep 24, 2020

Mar 20, 2019

16/360006

- Redmond WA, US
Tianyi CHEN - Redmond WA, US
Sheng YI - Bellevue WA, US

G06K 9/00
G06T 7/11
G06N 3/04

An ink parsing system receives ink strokes at an inking device input and render the received ink strokes into an image in a pixel space. Writing strokes are detected in the image and labeled. Pixels corresponding to the labeled writing strokes are removed from the image. Drawing strokes in the image having the removed pixels are detected using and labeled. Writing objects and drawing objects corresponding, respectively, to the labeled writing strokes and the labeled drawing strokes are output. A digital ink parsing pipeline is thereby provided having accurate ink stroke detection and segmentation.