Deepmind Technologies Limited

Wang Fumin

Nicolas Manfred Otto Heess

Mel Vecerik

Todd Andrew Hester

Jonathan Karl Scholz

Martin Riedmiller

Bilal Piot

Thomas Lampe

Thomas Rothoerl

Olivier Claude Pietquin

Olivier Pietquin

Matej Vecerik

An off-policy reinforcement learning actor-critic neural network system configured to select actions from a continuous action space to be performed by an agent interacting with an environment to perform a task. An observation defines environment state data and reward data. The system has an actor neural network which learns a policy function mapping the state data to action data. A critic neural network learns an action-value (Q) function. A replay buffer stores tuples of the state data, the action data, the reward data and new state data. The replay buffer also includes demonstration transition data comprising a set of the tuples from a demonstration of the task within the environment. The neural network system is configured to train the actor neural network and the critic neural network off-policy using stored tuples from the replay buffer comprising tuples both from operation of the system and from the demonstration transition data.

Training action selection neural networks using apprenticeship

Growing community of inventors

London, United Kingdom

Wang Fumin