Deepmind technologies limited (20250117654). DISTRIBUTIONAL REINFORCEMENT LEARNING USING QUANTILE FUNCTION NEURAL NETWORKS
DISTRIBUTIONAL REINFORCEMENT LEARNING USING QUANTILE FUNCTION NEURAL NETWORKS
Organization Name
Inventor(s)
William Clinton Dabney of London GB
DISTRIBUTIONAL REINFORCEMENT LEARNING USING QUANTILE FUNCTION NEURAL NETWORKS
This abstract first appeared for US patent application 20250117654 titled 'DISTRIBUTIONAL REINFORCEMENT LEARNING USING QUANTILE FUNCTION NEURAL NETWORKS
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent interacting with an environment. in one aspect, a method comprises: receiving a current observation; for each action of a plurality of actions: randomly sampling one or more probability values; for each probability value: processing the action, the current observation, and the probability value using a quantile function network to generate an estimated quantile value for the probability value with respect to a probability distribution over possible returns that would result from the agent performing the action in response to the current observation; determining a measure of central tendency of the one or more estimated quantile values; and selecting an action to be performed by the agent in response to the current observation using the measures of central tendency for the actions.
