  An efficient inverse reinforcement learning for generating trajectories is
proposed based of 2D and 3D activity forecasting. We modify reward function
with $L_p$ norm and propose convolution into value iteration steps, which is
called convolutional value iteration. Experimental results with seabird
trajectories (43 for training and 10 for test), our method is best in terms of
MHD error and performs fastest. Generated trajectories for interpolating
missing parts of trajectories look much similar to real seabird trajectories
than those by the previous works.
