Sony Group Corporation

Honda Motor Co., Ltd.

Sony Corporation of America

Kaushik Subramanian

James MacGlashan

Mark Ring

Satinder Singh Baveja

Roberto Capobianco

Varun Kompella

Peter R Wurman

Kikuo Fujimura

David Francis Isele

Gholamreza Rahimi

Akansel Cosgun

Peter Stone

Samuel Barrett

A method and system for teaching an artificial intelligent agent where the agent can be placed in a state that it would like it to learn how to achieve. By giving the agent several examples, it can learn to identify what is important about these example states. Once the agent has the ability to recognize a goal configuration, it can use that information to then learn how to achieve the goal states on its own. An agent may be provided with positive and negative examples to demonstrate a goal configuration. Once the agent has learned certain goal configurations, the agent can learn policies and skills that achieve the learned goal configuration. The agent may create a collection of these policies and skills from which to select based on a particular command or state.

Method and system for an intelligent artificial agent

Systems and methods for generating instructions for a vehicle to navigate an unsignaled intersection are provided. The method may include: generating an expected return over a sequence of actions of the vehicle; determining an optimal policy by selecting an action with a maximum value for the vehicle; executing dynamic frame skipping to expedite learning a repeated action of the vehicle; prioritize an experience replay by utilizing an experience replay buffer to break correlations between sequential steps of the vehicle; generate a plurality of state-action representations based on at least one of the expected return, the optimal policy, the dynamic frame skipping, or the prioritized experience replay; generate the instructions for navigating the unsignaled intersection based on the plurality of state-action representations; and transmit the instructions for navigating the unsignaled intersection to the vehicle such that the vehicle executes the instructions to navigate the unsignaled intersection.

Systems and methods for generating instructions for navigating intersections with autonomous vehicles

A method and system for teaching an artificial intelligent agent includes giving the agent several examples where it can learn to identify what is important about these example states. Once the agent has the ability to recognize a goal configuration, it can use that information to then learn how to achieve the goal states on its own. An agent may be provided with positive and negative examples to demonstrate a goal configuration. Once the agent has learned certain goal configurations, the agent can learn an option to achieve the goal configuration and a distance function that predicts at least one of a distance and a duration to the goal configuration under the learned option. This distance function prediction may be incorporated as a state feature of the agent.

Method and system for continual learning in an intelligent artificial agent

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Richmond, CA, United States of America

Kaushik Subramanian