Mitigating Catastrophic Forgetting with Complementary Layered Learning

Abstract

Catastrophic forgetting is a stability–plasticity imbalance that causes a machine learner to lose previously gained knowledge that is critical for performing a task. The imbalance occurs in transfer learning, negatively affecting the learner’s performance, particularly in neural networks and layered learning. This work proposes a complementary learning technique that introduces long- and short-term memory to layered learning to reduce the negative effects of catastrophic forgetting. In particular, this work proposes the dual memory system in the non-neural network approaches of evolutionary computation and Q-learning instances of layered learning because these techniques are used to develop decision-making capabilities for physical robots. Experiments evaluate the new learning augmentation in a multi-agent system simulation, where autonomous unmanned aerial vehicles learn to collaborate and maneuver to survey an area effectively. Through these direct-policy and value-based learning experiments, the proposed complementary layered learning is demonstrated to significantly improve task performance over standard layered learning, successfully balancing stability and plasticity.