Value-based Bayesian Meta-reinforcement Learning and Traffic Signal Control

Reinforcement learning methods for traffic signal control has gained increasing interests recently and achieved better performances compared with traditional transportation methods. However, reinforcement learning based methods usually requires heavy training data and computational resources which largely limit its application in real-world traffic signal control. This makes meta-learning, which enables data-efficient and fast-adaptation training by leveraging the knowledge of previous learning experiences, catches attentions in traffic signal control. In this paper, we propose a novel value-based Bayesian meta-reinforcement learning framework BM-DQN to robustly speed up the learning process in new scenarios by utilizing well-trained prior knowledge learned from existing scenarios. This framework based on our proposed fast-adaptation variation to Gradient-EM Bayesian Meta-learning and the fast update advantage of DQN, which allows fast adaptation to new scenarios with continual learning ability and robustness to uncertainty. The experiments on 2D navigation and traffic signal control show that our proposed framework adapts more quickly and robustly in new scenarios than previous methods, and specifically, much better continual learning ability in heterogeneous scenarios.