  This paper presents a general framework for exploiting the representational
capacity of neural networks to approximate complex, nonlinear reward functions
in the context of solving the inverse reinforcement learning (IRL) problem. We
show in this context that the Maximum Entropy paradigm for IRL lends itself
naturally to the efficient training of deep architectures. At test time, the
approach leads to a computational complexity independent of the number of
demonstrations, which makes it especially well-suited for applications in
life-long learning scenarios. Our approach achieves performance commensurate to
the state-of-the-art on existing benchmarks while exceeding on an alternative
benchmark based on highly varying reward structures. Finally, we extend the
basic architecture - which is equivalent to a simplified subclass of Fully
Convolutional Neural Networks (FCNNs) with width one - to include larger
convolutions in order to eliminate dependency on precomputed spatial features
and work on raw input representations.
