Name
Abstract | ||
|---|---|---|
Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to maintain an oracle $D$ that discriminates between the expertu0027s data distribution and that of the generative model $G$. The generative model is trained to capture the expertu0027s distribution by maximizing the probability of $D$ misclassifying the data it generates. Overall, the system is emph{differentiable} end-to-end and is trained using basic backpropagation. This type of learning was successfully applied to the problem of policy imitation in a model-free setup. However, a model-free approach does not allow the system to be differentiable, which requires the use of high-variance gradient estimations. In this paper we introduce the Model based Adversarial Imitation Learning (MAIL) algorithm. A model-based approach for the problem of adversarial imitation learning. We show how to use a forward model to make the system fully differentiable, which enables us to train policies using the (stochastic) gradient of $D$. Moreover, our approach requires relatively few environment interactions, and fewer hyper-parameters to tune. We test our method on the MuJoCo physics simulator and report initial results that surpass the current state-of-the-art. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Machine Learning | Computer science,Oracle,Differentiable function,Imitation,Artificial intelligence,Generative grammar,Backpropagation,Imitation learning,Machine learning,Adversarial system,Generative model |
DocType | Volume | Citations |
Journal | abs/1612.02179 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nir Baram | 1 | 17 | 2.71 |
| Oron Anschel | 2 | 17 | 2.71 |
| Shie Mannor | 3 | 3340 | 285.45 |