  We provide new perspectives and inference algorithms for Maximum Entropy
(MaxEnt) Inverse Reinforcement Learning (IRL), which provides a principled
method to find a most non-committal reward function consistent with given
expert demonstrations, among many consistent reward functions.
  We first present a generalized MaxEnt formulation based on minimizing a
KL-divergence instead of maximizing an entropy. This improves the previous
heuristic derivation of the MaxEnt IRL model (for stochastic MDPs), allows a
unified view of MaxEnt IRL and Relative Entropy IRL, and leads to a model-free
learning algorithm for the MaxEnt IRL model. Second, a careful review of
existing inference algorithms and implementations showed that they
approximately compute the marginals required for learning the model. We provide
examples to illustrate this, and present an efficient and exact inference
algorithm. Our algorithm can handle variable length demonstrations; in
addition, while a basic version takes time quadratic in the maximum
demonstration length L, an improved version of this algorithm reduces this to
linear using a padding trick.
  Experiments show that our exact algorithm improves reward learning as
compared to the approximate ones. Furthermore, our algorithm scales up to a
large, real-world dataset involving driver behaviour forecasting. We provide an
optimized implementation compatible with the OpenAI Gym interface. Our new
insight and algorithms could possibly lead to further interest and exploration
of the original MaxEnt IRL model.
