On Computation and Generalization of Generative Adversarial Imitation Learning

by   Minshuo Chen, et al.

Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies. Different from Reinforcement Learning (RL), GAIL takes advantage of demonstration data by experts (e.g., human), and learns both the policy and reward function of the unknown environment. Despite the significant empirical progresses, the theory behind GAIL is still largely unknown. The major difficulty comes from the underlying temporal dependency of the demonstration data and the minimax computational formulation of GAIL without convex-concave structure. To bridge such a gap between theory and practice, this paper investigates the theoretical properties of GAIL. Specifically, we show: (1) For GAIL with general reward parameterization, the generalization can be guaranteed as long as the class of the reward functions is properly controlled; (2) For GAIL, where the reward is parameterized as a reproducing kernel function, GAIL can be efficiently solved by stochastic first order optimization algorithms, which attain sublinear convergence to a stationary solution. To the best of our knowledge, these are the first results on statistical and computational guarantees of imitation learning with reward/policy function approximation. Numerical experiments are provided to support our analysis.



There are no comments yet.


page 1

page 2

page 3

page 4


Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate

Generative adversarial imitation learning (GAIL) demonstrates tremendous...

Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation

In generative adversarial imitation learning (GAIL), the agent aims to l...

On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator

We study the global convergence of generative adversarial imitation lear...

Generative Adversarial Self-Imitation Learning

This paper explores a simple regularizer for reinforcement learning by p...

What is the Reward for Handwriting? – Handwriting Generation by Imitation Learning

Analyzing the handwriting generation process is an important issue and h...

Challenging Common Assumptions in Convex Reinforcement Learning

The classic Reinforcement Learning (RL) formulation concerns the maximiz...

Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning

Generative adversarial imitation learning (GAIL) has attracted increasin...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.