Model-free reinforcement learning

Abstract

A system for generating a model-free reinforcement learning policy may include a processor, a memory, and a simulator. The simulator may be implemented via the processor and the memory. The simulator may generate a simulated traffic scenario including two or more lanes, an ego-vehicle, a dead end position, and one or more traffic participants. The dead end position may be a position by which a lane change for the ego-vehicle may be desired. The simulated traffic scenario may be associated with an occupancy map, a relative velocity map, a relative displacement map, and a relative heading map at each time step within the simulated traffic scenario. The simulator may model the ego-vehicle and one or more of the traffic participants using a kinematic bicycle model. The simulator may build a policy based on the simulated traffic scenario using an actor-critic network. The policy may be implemented on an autonomous vehicle.