Deepmind Technologies Limited

Sudarshan Kumaran

Raia Thais Hadsell

Andrea Banino

Koray Kavukcuoglu

Razvan Pascanu

Hubert Josef Soyer

Misha Man Ray Denil

Benigno Uria-Martínez

Laurent Sifre

Fabio Viola

Piotr Wojciech Mirowski

Andrew James Ballard

Rostislav Goroshin

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a grid cell neural network and an action selection neural network. The grid cell network is configured to: receive an input comprising data characterizing a velocity of the agent; process the input to generate a grid cell representation; and process the grid cell representation to generate an estimate of a position of the agent in the environment; the action selection neural network is configured to: receive an input comprising a grid cell representation and an observation characterizing a state of the environment; and process the input to generate an action selection network output.

Performing navigation tasks using grid codes

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. In one aspect, a method of training an action selection policy neural network for use in selecting actions to be performed by an agent navigating through an environment to accomplish one or more goals comprises: receiving an observation image characterizing a current state of the environment; processing, using the action selection policy neural network, an input comprising the observation image to generate an action selection output; processing, using a geometry-prediction neural network, an intermediate output generated by the action selection policy neural network to predict a value of a feature of a geometry of the environment when in the current state; and backpropagating a gradient of a geometry-based auxiliary loss into the action selection policy neural network to determine a geometry-based auxiliary update for current values of the network parameters.

Environment navigation using reinforcement learning

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London, United Kingdom

Sudarshan Kumaran