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In cooperation, the workers must know how co-workers behave. However, anagent's policy, which is embedded in a statistical machine learning model, ishard to understand, and requires much time and knowledge to comprehend.Therefore, it is difficult for people to predict the behavior of machinelearning robots, which makes Human Robot Cooperation challenging. In thispaper, we propose Instruction-based Behavior Explanation (IBE), a method toexplain an autonomous agent's future behavior. In IBE, an agent canautonomously acquire the expressions to explain its own behavior by reusing theinstructions given by a human expert to accelerate the learning of the agent'spolicy. IBE also enables a developmental agent, whose policy may change duringthe cooperation, to explain its own behavior with sufficient time granularity.

Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents