Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to make model trained in virtual environment be workable in real world. The proposed network can convert non-realistic virtual image input into a realistic one with similar scene structure. Given realistic frames as input, driving policy trained by reinforcement learning can nicely adapt to real world driving. Experiments show that our proposed virtual to real (VR) reinforcement learning (RL) works pretty well. To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data. Autonomous driving aims to make a vehicle sense its environment and navigate without human input. To achieve this goal, the most important task is to learn the driving policy that automatically outputs control signals for steering wheel, throttle, brake, etc., based on observed surroundings. The straight-forward idea is end-to-end supervised learning [3, 4] , which trains a neural network model mapping visual input directly to action output, and the training data is labeled image-action pairs. However, supervised approach usually requires large amount of data to train a model [31] that can generalize to different environments. Obtaining such amount of data is time consuming and requires significant human involvement. By contrast, reinforcement learning learns by a trial-and-error fashion, and does not require explicit supervision from human. Recently, reinforcement learning has been considered as a promising technique to learn driving policy due to its expertise in action planing [15, 23, 25] . However, reinforcement learning requires agents to interact with environments, and undesirable driving actions would happen. Training autonomous driving cars in real world will cause damages to vehicles and the surroundings. Therefore, most of current research in autonomous driving with reinforcement learning focus on simulations [15, 18, 25] rather than training in real world. While an agent trained with reinforcement learning achieves
The authors of REF proposed a network that can convert non-realistic virtual images into realistic ones with similar scene structure.
Virtual to Real Reinforcement Learning for Autonomous Driving
Proceedings of the British Machine Vision Conference (BMVC) 2017 (Spotlight)
Computer Science