  We address multi-modal trajectory forecasting of agents in unknown scenes by
formulating it as a planning problem. We present an approach consisting of
three models; a goal prediction model to identify potential goals of the agent,
an inverse reinforcement learning model to plan optimal paths to each goal, and
a trajectory generator to obtain future trajectories along the planned paths.
Analysis of predictions on the Stanford drone dataset, shows generalizability
of our approach to novel scenes.
