  We consider planning problems, that often arise in autonomous driving
applications, in which an agent should decide on immediate actions so as to
optimize a long term objective. For example, when a car tries to merge in a
roundabout it should decide on an immediate acceleration/braking command, while
the long term effect of the command is the success/failure of the merge. Such
problems are characterized by continuous state and action spaces, and by
interaction with multiple agents, whose behavior can be adversarial. We argue
that dual versions of the MDP framework (that depend on the value function and
the $Q$ function) are problematic for autonomous driving applications due to
the non Markovian of the natural state space representation, and due to the
continuous state and action spaces. We propose to tackle the planning task by
decomposing the problem into two phases: First, we apply supervised learning
for predicting the near future based on the present. We require that the
predictor will be differentiable with respect to the representation of the
present. Second, we model a full trajectory of the agent using a recurrent
neural network, where unexplained factors are modeled as (additive) input
nodes. This allows us to solve the long-term planning problem using supervised
learning techniques and direct optimization over the recurrent neural network.
Our approach enables us to learn robust policies by incorporating adversarial
elements to the environment.
