Microsoft Technology Licensing, LLC

Katja Hofmann

Sam Michael Devlin

Kamil Andrzej Ciosek

Sebastian Tschiatschek

Cheng Zhang

Yingzhen Li

Maximilian Igl

Matteo Venanzi

Filip Andrzej Radlinski

Adrian Kieron O'Grady

Luisa Maria Zintgraf

Anthony David Joseph Diggle

Andrew Philip Slowey

Janhavi Agrawal

Jay Nanavati

David Michael Bignell

Brian Brost

An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.

Behavior policy for a generalized reinforcement learning agent utilizing a weight of a loss function

In various examples there is a method performed by a computer-implemented agent in an environment. The method comprises storing a reinforcement learning policy for controlling the computer-implemented agent. The method also comprises storing a distribution as a latent representation of a belief of the computer-implemented agent about at least one other agent in the environment. The method involves executing the computer-implemented agent according to the policy conditioned on parameters characterizing the distribution.

Interactive agent and control using reinforcement learning

In various examples there is an information retrieval system which has a memory storing an index of items, each item having one or more features of a plurality of features. The index includes, for each item, an indication of the features of that item; and is configured at least according to the features. The memory stores a plurality of questions about the features of the items. The information retrieval system has a question selector that selects at least one of the questions on the basis of at least an expected information gain of the question with respect to the items. A search component searches the index using answers to the selected questions received from a user.

Information retrieval system

Generalized reinforcement learning agent

An apparatus is described for training a behavior of an agent in a physical or digital environment. The apparatus comprises a memory storing the location of at least one reward token in the environment. The location has been specified by a user. At least one processor executes the agent in the environment according to a behavior policy. The processor is configured to observe values of variables comprising: an observation of the agent, an action of the agent and any reward resulting from the reward token. The processor is configured to update the behavior policy using reinforcement learning according to the observed values.

Training behavior of an agent

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Cambridge, United Kingdom

Katja Hofmann