  Within the context of trajectory planning for autonomous vehicles this paper
proposes methods for efficient encoding of motion primitives in neural networks
on top of model-based and gradient-free reinforcement learning. It is
distinguished between 5 core aspects: system model, network architecture,
training algorithm, training tasks selection and hardware/software
implementation. For the system model, a kinematic (3-states-2-controls) and a
dynamic (16-states-2-controls) vehicle model are compared. For the network
architecture, 3 feedforward structures are compared including weighted skip
connections. For the training algorithm, virtual velocity constraints and
network scheduling are proposed. For the training tasks, different feature
vector selections are discussed. For the implementation, aspects of
gradient-free learning using 1 GPU and the handling of perturbation noise
therefore are discussed. The effects of proposed methods are illustrated in
experiments encoding up to 14625 motion primitives. The capabilities of tiny
neural networks with as few as 10 scalar parameters when scheduled on vehicle
velocity are emphasized.
