Patent ID: 11886992
Assignee: DEEPMIND TECHNOLOGIES LIMITED
Field: Digital communication (Electrical engineering)
Classification: CPC G  H | IPC G

Claim 14:
15. One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations for training a Q network used to select actions to be performed by an agent that interacts with an environment by receiving observations characterizing states of the environment and performing actions from a set of actions in response to the observations,
wherein the Q network is a deep neural network that is configured to receive as input an input observation and an input action and to generate an estimated future cumulative reward from the input in accordance with a set of parameters, and
wherein the operations comprise:
obtaining a plurality of experience tuples, wherein each experience tuple includes a training observation, an action performed by the agent in response to receiving the training observation, a reward received in response to the agent performing the action, and a next training observation that characterizes a next state of the environment; and
training the Q network on each of the experience tuples, comprising, for each experience tuple:
processing the training observation in the experience tuple and the action in the experience tuple using the Q network to determine a current estimated future cumulative reward for the experience tuple in accordance with current values of the parameters of the Q network;
for each action in the set of actions, processing the next observation in the experience tuple and the action using the Q network to determine a respective next estimated future cumulative reward for the action in accordance with current values of the parameters of the Q network;
selecting an action from the set of actions that has the highest next estimated future cumulative reward;
processing the next observation in the experience tuple and the selected action using a target Q network to determine a next target estimated future cumulative reward for the selected action in accordance with current values of parameters of the target Q network, wherein the target Q network has a same neural network architecture as the Q network but the current values of the parameters of the Q network are different from the current values of the parameters of the target Q network;
determining an error for the experience tuple from the reward in the experience tuple, the next target estimated future cumulative reward for the selected action, and the current estimated future cumulative reward; and
using the error for the experience tuple to update the current values of the parameters of the Q network.