… , non-linear inverse reinforcement learning (IRL) framework which exploits the capacity of
fully convolutional neural networks (FCNs) to represent the cost model underlying driving …

… the usage and common structures of cost functions. In section III, we introduce our approach
to inverse reinforcement learning of cost functions, before we describe the application of the …

… the cost, and second, the difficulty of learning the cost function under … capable of learning
arbitrary nonlinear cost functions, such as … Maximum entropy inverse reinforcement learning in …

… We present an algorithm that recovers an unknown cost function … cost function is a weighted
linear combination of features, and we are able to learn weights that result in a cost function …

… The goal of inverse reinforcement learning is to find a reward function for a Markov decision
… returns a reward function as well as the constructed features. The reward function can be …

… The method considers a reward function as negative of a cost function. Blue path depicts
the demonstrated trajectory, and green path shows the maximum return (or minimum cost) …

… Inverse reinforcement learning (IRL) is the problem of inferring the reward function of a
reinforcement learning (RL) agent from its observed behavior [1]. Despite wide-spread …

… if this operation is either costly or rises ethical questions). In … require a properly defined cost
function (or its evaluation on … to learn an optimal policy wrt the expert’s latent cost function. …

… a novel inverse reinforcement learning algorithm that enables learning cost functions by …
allows us to compute gradients of cost function parameters as a function of the inner loop policy …

… The early work on control-theoretic IOC was inspired by the the concept of inverse optimality
first studied by Kalman (1964). Similar to IRL, control-theoretic IOC estimates a cost function …