Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning
Implementing an autonomous vehicle that is able to output feasible, smooth and efficient trajectories is a long-standing challenge. Several approaches have been considered, roughly falling under two categories: rule-based and learning-based approaches. The rule-based approaches, while guaranteeing safety and feasibility, fall short when it comes to long-term planning and generalization. The learning-based approaches are able to account for long-term planning and generalization to unseen situations, but may fail to achieve smoothness, safety and the feasibility which rule-based approaches ensure. Hence, combining the two approaches is an evident step towards yielding the best compromise out of both. We propose a Reinforcement Learning-based approach, which learns target trajectory parameters for fully autonomous driving on highways. The trained agent outputs continuous trajectory parameters based on which a feasible polynomial-based trajectory is generated and executed. We compare the performance of our agent against four other highway driving agents. The experiments are conducted in the Sumo simulator, taking into consideration various realistic, dynamically changing highway scenarios, including surrounding vehicles with different driver behaviors. We demonstrate that our offline trained agent, with randomly collected data, learns to drive smoothly, achieving velocities as close as possible to the desired velocity, while outperforming the other agents. Code, training data and details available at: https://nrgit.informatik.uni-freiburg. de/branka.mirchevska/offline-rl-tp.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingOffline RLreinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space
Tactical decision making and strategic motion planning for autonomous highway driving are challenging due to the complication of predicting other road users' behaviors, diversity of environments, and complexity of the tr…
Decision MakingDeep Reinforcement LearningDiversityMotion Planning+5Safe Reinforcement Learning with Probabilistic Control Barrier Functions for Ramp Merging
Prior work has looked at applying reinforcement learning and imitation learning approaches to autonomous driving scenarios, but either the safety or the efficiency of the algorithm is compromised. With the use of control…
Autonomous DrivingImitation Learningreinforcement-learningReinforcement Learning+2Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision-making policy is constructed for autonomous vehi…
Autonomous DrivingAutonomous VehiclesDecision MakingDeep Reinforcement Learning+2Maximum Entropy Semi-Supervised Inverse Reinforcement Learning
A popular approach to apprenticeship learning (AL) is to formulate it as an inverse reinforcement learning (IRL) problem. The MaxEnt-IRL algorithm successfully integrates the maximum entropy principle into IRL and unlike…
Reinforcement LearningBeyond Reward: Offline Preference-guided Policy Optimization
This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online interaction or specification of reward f…
Offline RLreinforcement-learning