Federated Transfer Reinforcement Learning for Autonomous Driving

Xinle Liang 1, Y ang Liu 1, Tianjian Chen 1, Ming Liu 2 and Qiang Y ang 1 Abstract -- Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL models typically involves in a multi-step process: pre-training RL models on simulators, uploading the pre-trained model to real-life robots, and fine-tuning the weight parameters on robot vehicles. This sequential process is extremely time-consuming and more importantly, knowledge from the fine-tuned model stays local and can not be reused or leveraged collaboratively. T o tackle this problem, we present an online federated RL transfer process for real-time knowledge extraction where all the participant agents make corresponding actions with the knowledge learned by others, even when they are acting in very different environments. T o validate the effectiveness of the proposed approach, we constructed a real-life collision avoidance system with Microsoft Airsim simulator and NVIDIA JetsonTX2 car agents, which cooperatively learn from scratch to avoid collisions in indoor environment with obstacle objects. We demonstrate that with the proposed framework, the simulator car agents can transfer knowledge to the RC cars in real-time, with 27% increase in the average distance with obstacles and 42% decrease in the collision counts. I. INTRODUCTION Recent Reinforcement Learning (RL) researches in autonomous robots have achieved significant performance improvement by employing distributed architecture for decentralized agents [1], [2], which is termed as Distributed Reinforcement Learning (DRL). However, most existing DRL frameworks consider only synchronous learning with a constant environment.