We draw our inspiration from recent progress in inverse reinforcement learning (Ho and Ermon, 2016;Finn et al., 2016;Fu et al., 2017) and propose the AREL algorithm to learn a more intelligent reward function.
we first incorporate a Boltzmann distribution to associate reward learning with distribution approximation, then design the adversarial process with two models -a policy model and a reward model.