Information processing method and information processing device

Patent metadata

Field	Value
Publication number	US-2020242512-A1
Application number	US-202016737949-A
Country	US
Kind code	A1
Filing date	Jan 9, 2020
Priority date	Jan 24, 2019
Publication date	Jul 30, 2020
Grant date	—

Abstract

An information processing method comprises: generating an action sequence pair of a first action sequence of a first agent and a second action sequence of a second agent, the first and second action sequences performing an identical task; training a mapping model using the generated action sequence pair such that it is capable of generating an action sequence of the second agent according to an action sequence of the first agent; training a judgment model using the first action sequence of the first agent such that it is capable of judging whether a current action of an action sequence of the first agent is a last action of the action sequence; and constructing a mapping library using the trained mapping model and the trained judgment model, wherein the mapping library comprises a mapping from observation information of the second agent to an action sequence of the second agent.

First claim

1 . An information processing method for transferring processing knowledge of a first agent to a second agent, wherein the first agent is capable of performing a corresponding action sequence according to observation information of the first agent, the information processing method comprising steps of:
generating an action sequence pair of a first action sequence of the first agent and a second action sequence of the second agent, wherein the first action sequence and the second action sequence perform an identical task; training a mapping model using the generated action sequence pair, wherein the mapping model is capable of generating an action sequence of the second agent according to an action sequence of the first agent; training a judgment model using the first action sequence of the first agent, wherein the judgment model is capable of judging whether a current action of an action sequence of the first agent is a last action of the action sequence; and constructing a mapping library using the trained mapping model and the trained judgment model, wherein the mapping library comprises a mapping from observation information of the second agent to an action sequence of the second agent.

2 . The information processing method according to claim 1 , wherein a degree of freedom of an action of the first agent is different from a degree of freedom of an action of the second agent.

3 . The information processing method according to claim 1 , wherein the action sequence pairs which are different are constructed by using different tasks.

4 . The information processing method according to claim 1 , wherein the step of training the mapping model using the action sequence pair further comprises:
setting a first index of an action of the first agent, to represent the first action sequence of the first agent by a first index vector representing the first index; setting a second index of an action of the second agent, to represent the second action sequence of the second agent by a second index vector representing the second index; and training the mapping model using the first index vector and the second index vector.

5 . The information processing method according to claim 1 , wherein the step of training the judgment model using the first action sequence further comprises:
setting a first index of an action of the first agent, to represent the first action sequence of the first agent by a first index vector representing the first index; and training the judgment model using the first index vector.

6 . The information processing method according to claim 1 , wherein
the mapping model comprises an encoding unit and a decoding unit, the encoding unit is configured to encode an action sequence of the first agent to a length-fixed vector, and the decoding unit is configured to decode the length-fixed vector to an action sequence of the second agent.

7 . The information processing method according to claim 1 , wherein
the mapping model comprises an encoding unit and a decoding unit, the encoding unit is configured to encode an inverse sequence of an action sequence of the first agent to a length-fixed vector, and the decoding unit is configured to decode the length-fixed vector to an inverse sequence of an action sequence of the second agent.

8 . The information processing method according to claim 1 , wherein the step of constructing the mapping library using the trained mapping model and the trained judgment model further comprises:
performing, by the first agent, an action stream composed of an action sequence of the first agent, according to environmental information related to the observation information of the first agent; extracting the action sequence of the first agent from the action stream using the trained judgment model; generating an action sequence of the second agent according to the extracted action sequence of the first agent using the trained mapping model; and constructing a mapping from observation information of the second agent to an action sequence of the second agent.

9 . The information processing method according to claim 1 , further comprising:
training the second agent using the mapping library.

10 . An information processing device for transferring processing knowledge of a first agent to a second agent, wherein the first agent is capable of performing a corresponding action sequence according to observation information of the first agent, the information processing device comprising:
a generating unit configured to generate an action sequence pair of a first action sequence of the first agent and a second action sequence of the second agent, wherein the first action sequence and the second action sequence perform an identical task; a first training unit configured to train a mapping model using the generated action sequence pair, wherein the mapping model is capable of generating an action sequence of the second agent according to an action sequence of the first agent; a second training unit configured to train a judgment model using the first action sequence of the first agent, wherein the judgment model is capable of judging whether a current action of an action sequence of the first agent is a last action of the action sequence; and a constructing unit configured to construct a mapping library using the trained mapping model and the trained judgment model, wherein the mapping library comprises a mapping from observation information of the second agent to an action sequence of the second agent.

Assignees

• Fujitsu Ltd

Inventors

• Zhong Chaoliang
• Sun Jun

Classifications

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• B25J9/163 Primary

  learning, adaptive, model based, rule based expert control · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06F18/214 Primary

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06N3/09

  Supervised learning · CPC title