http://arxiv.org/abs/1507.04888
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 effi-
cient 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.
