Sony Group Corporation

Sony Corporation of America

Samuel Barrett

Peter R Wurman

James MacGlashan

Fabrizio Santini

Mark Ring

Piyush Khandelwal

Satinder Singh Baveja

Kaushik Subramanian

Peter Stone

Varun Kompella

Rory Douglas

Dion Whitehead

Roberto Capobianco

Leon Barrett

Thomas Walsh

Goker Erdogan

Leilani Gilpin

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 are used to adapt the coefficients of a proportional-integral-derivative (PID) controller through reinforcement learning. The approach for adapting PID coefficients can include an outer loop of reinforcement learning where the PID coefficients are tuned to changes in the environment and an inner loop of PID control for quickly reacting to changing errors. The outer loop can learn and adapt as the environment changes and be configured to only run at a predetermined frequency, after a given number of steps. The outer loop can use summary statistics about the error terms and any other information sensed about the environment to calculate an observation. This observation can be used to evaluate the next action, for example, by feeding it into a neural network representing the policy. The resulting action is the coefficients of the PID controller and the tunable parameters of things such as the filters.

Methods and systems to adapt PID coefficients through reinforcement learning

A user interface (UI), for analyzing model training runs, tracking and visualizing various aspects of machine learning experiments, can be used when training an artificial intelligent agent in, for example, a racing game environment. The UI can be web-based and can allow researchers to easily see the status of their experiments. The UI can include an experiment synchronized event viewer that can synchronizes visualizations, videos, and timeline/metrics graphs in the experiment. This viewer allows researchers to see how experiments unfold in great detail. The UI can further include experiment event annotations that can generate event annotations. These annotations can be displayed via the synchronized event viewer. The UI can be used to consider consolidated results across experiments and can further consider videos. For example, the UI can provide a reusable dashboard that can capture and compare metrics across multiple experiments.

User interface for operating artificial intelligence experiments

Growing community of inventors

Cambridge, MA, United States of America

Samuel Barrett