EVALUATING VARYING-SIZED ACTION SPACES USING REINFORCEMENT LEARNING

A set of actions corresponding to a particular state of the environment of a vehicle is identified. A respective encoding is generated for different actions of the set, using elements such as distinct colors to distinguish attributes such as target lane segments. Using the encodings as inputs to respective instances of a machine learning model, respective value metrics are estimated for each of the actions. One or more motion-control directives to implement a particular action selected using the value metrics are transmitted to motion-control subsystems of the vehicle.