Optimized reinforcement learning for artificial intelligence models
AssigneeSRI International
InventorChristopher Connolly
Life of this patent
Estimating the term…
Abstract
In an example, a system includes processing circuitry in communication with storage media, the processing circuitry configured to execute a reinforcement learning system configured to: perform, by an agent, an action within an environment; obtain one or more measurements from the environment, the one or more measurements resultant of the action performed by the agent; determine, based on the one or more measurements, a state of a domain model for the environment reached by the agent; and distribute credit, based at least in part on a reward associated with the state, across an explored state space for the domain model for the environment.
Claims20
A system comprising:
- processing circuitry in communication with storage media, the processing circuitry configured to execute a reinforcement learning system configured to:
- perform, by an agent, an action within an environment;
- obtain one or more measurements from the environment, the one or more measurements resultant of the action performed by the agent;
- determine, based on the one or more measurements, a state of a domain model for the environment reached by the agent;
- and
- distribute credit, based at least in part on a reward associated with the state, across an explored state space for the domain model for the environment.
A method comprising:
- performing, by an agent, an action within an environment;
- obtaining, by a reinforcement learning system, one or more measurements from the environment, the one or more measurements resultant of the action performed by the agent;
- determining, by the reinforcement learning system, based on the one or more measurements, a state of a domain model for the environment reached by the agent;
- and
- distributing credit, by the reinforcement learning system, based at least in part on a reward associated with the state, across an explored state space for the domain model for the environment.
Non-transitory computer-readable storage media having instructions encoded thereon, the instructions configured to cause processing circuitry to:
- perform, by an agent, an action within an environment;
- obtain one or more measurements from the environment, the one or more measurements resultant of the action performed by the agent;
- determine, based on the one or more measurements, a state of a domain model for the environment reached by the agent;
- and
- distribute credit, based at least in part on a reward associated with the state, across an explored state space for the domain model for the environment.
Dependencies are inferred from the claim text.