US 2023/0419187 A1 · REINFORCEMENT LEARNING FOR DIVERSE CONTENT GENERATION

Abstract

Methods, systems and computer program products are provided for content generation. A distribution of policies is defined based on an action space. Distribution parameters are received from a reinforcement learning (RL) algorithm. In turn, a policy is randomly sampled from the distribution of policies. A candidate content item is generated using the sampled policy. A quality of the candidate content item is measured based on a predefined quality criteria and a parameter model is adjusted as specified by the reinforcement learning algorithm to obtain a plurality of updated distribution parameters. Environment settings are passed to a trained parameter model to obtain a plurality of policy distribution parameters. A predetermined number of policies from the distribution of policies are then sampled and the plurality of environment settings are passed to the predetermined number of sampled policies to obtain at least one content item.

Claims18

Dependent claims

A content generator, comprising:

at least one processor coupled to a non-transitory storage device storing instructions which, when executed by the at least one processor, cause the at least one processor to:
randomly sample a policy from a distribution of policies to obtain a sampled policy;
generate a candidate content item using the sampled policy;
measure a quality of the candidate content item based on a predefined quality criteria;
and
adjust a parameter model as specified by a reinforcement learning algorithm to obtain a plurality of updated distribution parameters.

3 more elements

Dependencies are inferred from the claim text.

Reinforcement learning for diverse content generation

Life of this patent

Reinforcement learning for diverse content generation

Life of this patent

Abstract

Claims18