Invention Grant
- Patent Title: Online learning and vehicle control method based on reinforcement learning without active exploration
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Application No.: US15205558Application Date: 2016-07-08
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Publication No.: US10065654B2Publication Date: 2018-09-04
- Inventor: Tomoki Nishi
- Applicant: Toyota Motor Engineering & Manufacturing North America, Inc.
- Applicant Address: US TX Plano
- Assignee: Toyota Motor Engineering & Manufacturing North America, Inc.
- Current Assignee: Toyota Motor Engineering & Manufacturing North America, Inc.
- Current Assignee Address: US TX Plano
- Agency: Darrow Mustafa PC
- Agent Christopher G. Darrow
- Main IPC: G05D1/00
- IPC: G05D1/00 ; B60W50/06 ; G05B13/04 ; G05B13/02 ; B60W50/00

Abstract:
A computer-implemented method of adaptively controlling an autonomous operation of a vehicle is provided. The method includes steps of (a) in a critic network in a computing system configured to autonomously control the vehicle, determining, using samples of passively collected data and a state cost, an estimated average cost, and an approximated cost-to-go function that produces a minimum value for a cost-to-go of the vehicle when applied by an actor network; and (b) in an actor network in the computing system and operatively coupled to the critic network, determining a control input to apply to the vehicle that produces the minimum value for the cost-to-go, wherein the actor network is configured to determine the control input by estimating a noise level using the average cost, a cost-to-go determined from the approximated cost-to-go function, a control dynamics for a current state of the vehicle, and the passively collected data.
Public/Granted literature
- US20180009445A1 ONLINE LEARNING AND VEHICLE CONTROL METHOD BASED ON REINFORCEMENT LEARNING WITHOUT ACTIVE EXPLORATION Public/Granted day:2018-01-11
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