Patent application EP19157204A (EP), filed 2019-02-14
Publication number: EP3564861A1
Title: VISION-BASED SAMPLE-EFFICIENT REINFORCEMENT LEARNING FRAMEWORK FOR AUTONOMOUS DRIVING
Abstract: [0001]    A framework combines vision and sample-efficient reinforcement-learning based on guided policy search for autonomous driving. A controller extracts environmental information from vision and is trained to drive using reinforcement learning.
IPC codes: G06N  20/    00            A I
G06N   3/    00            A I
G06N   5/    04            A I
G06N   3/   006            A I
G06V  20/    58            A I
Claims: number: 1
text: A method comprising:
number: 2
text: The method of claim 1 wherein training the reinforcement learning controller for autonomous driving utilizes a first aspect to provide guidance regarding options to explore when making a decision and a second aspect to learn how to react based on vision information from the vision model.
number: 3
text: The method of claim 2 wherein the first aspect implements a guided policy search which iteratively optimizes a set of local policies for specific instances of a task and uses the local policies to train a general global policy usable across task instances and limits a search space.
number: 4
text: The method of claim 2 wherein deploying the reinforcement learning controller for autonomous driving utilizes the second aspect to learn how to react based on the vision information from the vision model.
number: 5
text: The method of claim 1 wherein the vision model is trained via images and/or videos acquired using one or more vehicle cameras.
number: 6
text: The method of claim 1 wherein training the reinforcement learning controller for autonomous driving utilizes labeled images which include fully or partially observed states.
number: 7
text: The method of claim 1 further comprising autonomously driving a vehicle using the reinforcement learning controller by sending a signal to at least one of a driving mechanism, a braking mechanism and an acceleration mechanism.
number: 8
text: A system comprising:
number: 9
text: The system of claim 8 wherein training the reinforcement learning controller for autonomous driving utilizes a first aspect to provide guidance regarding options to explore when making a decision and a second aspect to learn how to react based on vision information from the vision model.
number: 10
text: The system of claim 9 wherein the first aspect implements a guided policy search which iteratively optimizes a set of local policies for specific instances of a task and uses the local policies to train a general global policy usable across task instances and limits a search space.
number: 11
text: The system of claim 9 wherein utilizing the reinforcement learning controller for autonomous driving utilizes the second aspect to learn how to react based on the vision information from the vision model.
number: 12
text: The system of claim 8 wherein the vision model is trained via images and/or videos acquired using one or more vehicle cameras.
number: 13
text: The system of claim 8 wherein training the reinforcement learning controller for autonomous driving utilizes labeled images which include fully or partially observed states.
number: 14
text: The system of claim 8 wherein the reinforcement learning controller is further configured for autonomously driving a vehicle by sending a signal to at least one of a driving mechanism, a braking mechanism and an acceleration mechanism.
number: 15
text: A vehicle comprising:
Office action: search_report, dated 2019-11-06
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: US2015100530A1
ref_type: patent
severity: background
ref_id: US2017024643A1
ref_type: patent
severity: background
ref_id: US2018032082A1
ref_type: patent
severity: background
ref_id: US2018011488A1
ref_type: patent
severity: background
ref_id: - MICHEL BREYER ET AL: "Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 13 March 2018 (2018-03-13), XP081014586
ref_type: npl
severity: background
ref_id: US2017024643
ref_type: patent
severity: novelty_destroying
ref_id: US2018011488
ref_type: patent
severity: background
ref_id: US2018032082
ref_type: patent
severity: background
ref_id: - MICHEL BREYER ET AL, "Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20180313), XP081014586 [I] 1-15 * abstract * * page  4, column  le, line  13  - column  ri, line  16 * * figure 2 *
ref_type: npl
severity: background
Final disposition: pending (2019-11-06)