Explainable deep reinforcement learning using a factorized function
AssigneePalo Alto Research Center Incorporated
InventorRobert Price
Life of this patent
Estimating the term…
Abstract
A policy based on a compound reward function is learned through a reinforcement learning algorithm at a learning network. The policy is used to choose an action of a plurality of possible actions. A state-action value network is established for each of the two or more reward terms. The state-action value networks are separated from the learning network. A human-understandable output is produced to explain why the action was taken based on each of the state action value networks.
Claims20
A method for providing human understandable explanations for an action in a machine reinforcement learning framework comprising:
- learning, through a reinforcement learning algorithm at a learning network, a policy based on a compound reward function, the compound reward function comprising a sum of two or more reward terms;
- using the policy to choose an action of a plurality of possible actions;
- establishing a state-action value network for each of the two or more reward terms, the state-action value networks separated from the learning network;
- and
- producing a human-understandable output to explain why the action was taken based on each of the state action value networks.
A system comprising:
- a processor;
- and
- a memory storing computer program instructions which when executed by the processor cause the processor to perform operations comprising:
- learning, through a reinforcement learning algorithm at a learning network, a policy based on a compound reward function, the compound reward function comprising a sum of two or more reward terms;
- using the policy to choose an action of a plurality of possible actions;
- establishing a state-action value network for each of the two or more reward terms, the state-action value networks separated from the learning network;
- and
- producing a human-understandable output to explain why the action was taken based on each of the state action value networks.
Dependencies are inferred from the claim text.