Complementary reinforcement learning towards explainable agents
This addresses the need for explainable AI to deploy reinforcement learning in a wider range of applications, though it appears incremental as it builds on existing NN-based RL agents.
The paper tackles the problem of incomprehensible decision-making in neural network-based reinforcement learning agents by proposing a method to derive a secondary comprehensible agent based on simple rules, with empirical evaluation supporting its feasibility.
Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples. That is, RL agents can learn, as we learn. Given the importance of learning in our intelligence, RL has been thought to be one of key components to general artificial intelligence, and recent breakthroughs in deep reinforcement learning suggest that neural networks (NN) are natural platforms for RL agents. However, despite the efficiency and versatility of NN-based RL agents, their decision-making remains incomprehensible, reducing their utilities. To deploy RL into a wider range of applications, it is imperative to develop explainable NN-based RL agents. Here, we propose a method to derive a secondary comprehensible agent from a NN-based RL agent, whose decision-makings are based on simple rules. Our empirical evaluation of this secondary agent's performance supports the possibility of building a comprehensible and transparent agent using a NN-based RL agent.