MULTI-LEVEL INTROSPECTION FRAMEWORK FOR EXPLAINABLE REINFORCEMENT LEARNING AGENTS

摘要

Techniques are disclosed for applying a multi-level introspection framework to interaction data characterizing a history of interaction of a reinforcement learning agent with an environment. The framework may apply statistical analysis and machine learning methods to interaction data collected during the RL agent's interaction with the environment. The framework may include a first (“environment”) level that analyzes characteristics of one or more tasks to be solved by the RL agent to generate elements, a second (“interaction”) level that analyzes actions of the RL agent when interacting with the environment to generate elements, and a third (“meta-analysis”) level that generates elements by analyzing combinations of elements generated by the first level and elements generated by the second level.