IEEE

MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees

Abstract: While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network makes decisions in an opaque manner that hinders ...
IEEE

DTAME: A Interpretable and Efficient Approach for ABAC Policy Mining and Evaluation Using Decision Trees

Abstract: Attribute-Based Access Control (ABAC) has been chosen to replace the traditional access control model due to its dynamics, flexibility and scalability recently. However, during the migration ...