Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture
Chin-Jui Chang Yu-Wei Chu Chao-Hsien Ting Hao-Kang Liu Zhang-Wei Hong Chun-Yi Lee Chin-Jui Chang Yu-Wei Chu Chao-Hsien Ting Hao-Kang Liu Zhang-Wei Hong Chun-Yi Lee
Deep reinforcement learning (DRL) has been demonstrated to provide promising results in several challenging decision making and control tasks. However, the required inference costs of deep neural networks (DNNs) could prevent DRL from being applied to mobile robots which cannot afford high energy-consuming computations. To enable DRL methods to be affordable in such energy-limited platforms, we pr...