System and method for unsupervised concept extraction from reinforcement learning agents

A method for performance of agent-based machine learning with unsupervised concept extraction from reinforcement learning agents, the method comprising acts of:

• training a reinforcement learning (RL) agent on an image processing task;
• extracting a dataset of a plurality of states and corresponding actions from the RL agent, the plurality of states corresponding to a plurality of image frames;
• perturbing each image frame by blurring each pixel in the image frame and focusing the blur around each pixel using a Gaussian filter centered on the pixel;
• processing each image frame with the retrained RL agent to obtain an output;
• comparing the output to the image frame prior to perturbation to determine a saliency score for each pixel in each image frame;
• extracting, during action selection by the RL agent, a plurality of image patches of the input space based on the saliency score;
• clustering, using an unsupervised clustering algorithm, portions of a first set of inputs based on similarity to the plurality of image patches, thereby generating a first set of clusters having cluster centers;
• and
• providing non-semantic concept labels as distances to the cluster centers for each state in the dataset.

A computer program product for performance of agent-based machine learning with unsupervised concept extraction from reinforcement learning agents, the computer program product comprising:

• a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions by one or more processors, the one or more processors perform operations of:
• training a reinforcement learning (RL) agent on an image processing task;
• extracting a dataset of a plurality of states and corresponding actions from the RL agent, the plurality of states corresponding to a plurality of image frames;
• perturbing each image frame by blurring each pixel in the image frame and focusing the blur around each pixel using a Gaussian filter centered on the pixel;
• processing each image frame with the retrained RL agent to obtain an output;
• comparing the output to the image frame prior to perturbation to determine a saliency score for each pixel in each image frame;
• extracting, during action selection by the RL agent, a plurality of image patches of the input space based on the saliency score;
• clustering, using an unsupervised clustering algorithm, portions of a first set of inputs based on similarity to the plurality of image patches, thereby generating a first set of clusters having cluster centers;
• and
• providing non-semantic concept labels as distances to the cluster centers for each state in the dataset.