Deepmind Technologies Limited

Matteo Hessel

Tom Schaul

Hado Philip Van Hasselt

David Silver

Satinder Singh Baveja

Zhongwen Xu

Junhyuk Oh

Daniel J Mankowitz

Iurii Kemaev

Tom Ben Zion Zahavy

A method of training an action selection neural network for controlling an agent interacting with an environment to perform different tasks is described. The method includes obtaining a first trajectory of transitions generated while the agent was performing an episode of the first task from multiple tasks; and training the action selection neural network on the first trajectory to adjust the control policies for the multiple tasks. The training includes, for each transition in the first trajectory: generating respective policy outputs for the initial observation in the transition for each task in a subset of tasks that includes the first task and one other task; generating respective target policy outputs for each task using the reward in the transition, and determining an update to the current parameter values based on, for each task, a gradient of a loss between the policy output and the target policy output for the task.

Continual reinforcement learning with a multi-task agent

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for prediction of an outcome related to an environment. In one aspect, a system comprises a state representation neural network that is configured to: receive an observation characterizing a state of an environment being interacted with by an agent and process the observation to generate an internal state representation of the environment state; a prediction neural network that is configured to receive a current internal state representation of a current environment state and process the current internal state representation to generate a predicted subsequent state representation of a subsequent state of the environment and a predicted reward for the subsequent state; and a value prediction neural network that is configured to receive a current internal state representation of a current environment state and process the current internal state representation to generate a value prediction.

Environment prediction using reinforcement learning

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London, United Kingdom

Matteo Hessel