Trajectory-based explainability framework for reinforcement learning models

A system comprising:

• a memory component;
• and
• one or more processing devices coupled to the memory component, the one or more processing devices to perform operations comprising:
• generating a reinforcement learning decision utilizing a reinforcement learning agent trained from a plurality of trajectories;
• determining, utilizing a clustering algorithm, trajectory clusters from the plurality of trajectories;
• generating a plurality of complementary target data sets by individually removing target trajectory clusters for the plurality of complementary target data sets;
• training test reinforcement learning agents utilizing the plurality of complementary target data sets;
• and
• generating a cluster attribution for the reinforcement learning agent by comparing reinforcement learning decisions of the test reinforcement learning agents with the reinforcement learning decision of the reinforcement learning agent.

A non-transitory computer readable medium storing executable instructions which, when executed by a processing device, cause the processing device to perform operations comprising:

• generating, utilizing a clustering algorithm, trajectory clusters from trajectories utilized to train a reinforcement learning agent;
• generating a complementary target data set by removing a target trajectory cluster from the trajectory clusters;
• training a test reinforcement learning agent utilizing the complementary target data set;
• and
• generating a cluster attribution for the reinforcement learning agent by comparing a result of the test reinforcement learning agent and a result of the reinforcement learning agent.