Highway Environment Model for Reinforcement Learning ?

摘要

The paper presents a microscopic highway simulation model, built as an environment for the development of different machine learning based autonomous vehicle controllers. The environment is based on the popular OpenAI Gym framework, hence it can be easily integrated into multiple projects. The traffic flow is operated by classic microscopic models, while the agent’s vehicle uses a rigid kinematic single-track model, with either continuous or discrete action spaces. The environment also provides a simple high-level sensor model, where the state of the agent and its surroundings are part of the observation. To aid the learning process, multiple reward functions are also provided.