Generative adversarial networks(GAN): a class of generative models in which a generator is trained to optimize a cost function that is being simultaneously learned by a discriminator.
Learning the cost function underlying observed behavior is known as IRL.
Certain IRL methods are in fact mathematically equivalent to GANs.
This paper demonstrates an equivalence between a sample-based algorithm for maximum entropy IRL and a GAN in which the generator's density can be evaluated and is provided as an additional input to the discriminator.
maximum entropy IRL is a special case of an energy-based model.
This paper highlights the connection between GANs, IRL, and EBM(energy-based model).
GAN can be viewed as a sample-based algorithm for the MaxEnt IRL problem.
Generative adversarial networks(GAN): a class of generative models in which a generator is trained to optimize a cost function that is being simultaneously learned by a discriminator.
Learning the cost function underlying observed behavior is known as IRL. Certain IRL methods are in fact mathematically equivalent to GANs.
This paper demonstrates an equivalence between a sample-based algorithm for maximum entropy IRL and a GAN in which the generator's density can be evaluated and is provided as an additional input to the discriminator. maximum entropy IRL is a special case of an energy-based model. This paper highlights the connection between GANs, IRL, and EBM(energy-based model).
GAN can be viewed as a sample-based algorithm for the MaxEnt IRL problem.
GAIL: combing GAN with RL.======MaxEnt IRL