The invention discloses attention-based hierarchical lane change deep reinforcement learning. Systems and methods are provided autonomous driving policy generation. The system can include a set of autonomous driver agents, and a driving policy generation module that includes a set of driving policy learner modules for generating and improving policies based on the collective experiences collectedby the driver agents. The driver agents can collect driving experiences to create a knowledge base. The driving policy learner modules can process the collective driving experiences to extract drivingpolicies. The driver agents can be trained via the driving policy learner modules in a parallel and distributed manner to find novel and efficient driving policies and behaviors faster and more efficiently. Parallel and distributed learning can enable accelerated training of multiple autonomous intelligent driver agents.
Attention-based hierarchical lane change deep reinforcement learning