Deep deformable Q-Network
Beibei Jin, Jianing Yang, Xiangsheng Huang, Dawar Burhan Khan · Proceedings of the International Conference on Web Intelligence · 2017
The performance of Deep Reinforcement Learning (DRL) algorithms is usually constrained by instability and variability. In this work, we present an extension of Deep Q-Network (DQN) called Deep Deformable Q-Network which is based on deformable convolution mechanisms. The new algorithm can readily be built on existing models and can be easily trained end-to-end by standard back-propagation. Extensive experiments on the Atari games validate the feasibility and effectiveness of the proposed Deep Deformable Q-Network.