# Causal Imitation Learning With Unobserved Confounders

NIPS 2020, 2020.

EI

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摘要：

One of the common ways children learn is by mimicking adults. Imitation learning focuses on learning policies with suitable performance from demonstrations generated by an expert, with an unspecified performance measure, and unobserved reward signal. Popular methods for imitation learning start by either directly mimicking the behavior po...更多

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简介

- A unifying theme of Artificial Intelligence is to learn a policy from observations in an unknown environment such that a suitable level of performance is achieved [33, Ch. 1.1].
- Algorithms, and estimation methods have been developed to solve this problem [29, 36, 3, 6, 35, 32, 44]
- In many applications, it is not clear which performance measure the demonstrator is optimizing.
- The reward signal is not labeled and accessible in the observed expert’s trajectories
- In such settings, the performance of candidate policies is not uniquely discernible from the observational data due to latent outcomes, even when infinitely many samples are gathered, complicating efforts to learn policy with satisfactory performance

重点内容

- A unifying theme of Artificial Intelligence is to learn a policy from observations in an unknown environment such that a suitable level of performance is achieved [33, Ch. 1.1]
- (1) We introduce a complete graphical criterion for determining the feasibility of imitation from demonstration data and qualitative knowledge about the data-generating process represented as a causal graph
- We introduce optimization procedures to solve for an imitating policy at Step 5 of IMITATE algorithm
- We investigate the imitation learning in the semantics of structural causal models
- We provide a graphical criterion that is complete for determining the feasibility of learning an imitating policy that mimics the expert’s performance
- An efficient algorithm is introduced which finds an imitating policy, by exploiting quantitative knowledge contained in the observational data and the presence of surrogate endpoints

方法

- The authors demonstrate the algorithms on several synthetic datasets, including highD [18] consisting of natural trajectories of human driven vehicles, and on MNIST digits.
- The authors test the causal imitation method: the authors apply Thm. 2 when there exists an ⇡-backdoor admissible set; otherwise, Alg. 1 is used to leverage the observational distribution.
- The authors found that the algorithms consistently imitate distributions over the expert’s reward in imitable (p-imitable) cases; and p-imitable instances commonly exist.
- The authors obtain policies for the causal and naive imitators training two separate GANs. Distributions P (y|do(⇡)) induced by all algorithms are reported in Fig. 4b.

结论

- The goal is to find an imitating policy that mimics the expert behaviors from combinations of demonstration data and qualitative knowledge about the data-generating process represented as a causal diagram.
- The authors provide a graphical criterion that is complete for determining the feasibility of learning an imitating policy that mimics the expert’s performance.
- An efficient algorithm is introduced which finds an imitating policy, by exploiting quantitative knowledge contained in the observational data and the presence of surrogate endpoints.
- The authors propose a practical procedure for estimating such an imitating policy from observed trajectories of the expert’s demonstrations

总结

## Introduction:

A unifying theme of Artificial Intelligence is to learn a policy from observations in an unknown environment such that a suitable level of performance is achieved [33, Ch. 1.1].- Algorithms, and estimation methods have been developed to solve this problem [29, 36, 3, 6, 35, 32, 44]
- In many applications, it is not clear which performance measure the demonstrator is optimizing.
- The reward signal is not labeled and accessible in the observed expert’s trajectories
- In such settings, the performance of candidate policies is not uniquely discernible from the observational data due to latent outcomes, even when infinitely many samples are gathered, complicating efforts to learn policy with satisfactory performance
## Objectives:

The authors' goal is to learn an efficient policy to decide the value of an action variable X 2 O.## Methods:

The authors demonstrate the algorithms on several synthetic datasets, including highD [18] consisting of natural trajectories of human driven vehicles, and on MNIST digits.- The authors test the causal imitation method: the authors apply Thm. 2 when there exists an ⇡-backdoor admissible set; otherwise, Alg. 1 is used to leverage the observational distribution.
- The authors found that the algorithms consistently imitate distributions over the expert’s reward in imitable (p-imitable) cases; and p-imitable instances commonly exist.
- The authors obtain policies for the causal and naive imitators training two separate GANs. Distributions P (y|do(⇡)) induced by all algorithms are reported in Fig. 4b.
## Conclusion:

The goal is to find an imitating policy that mimics the expert behaviors from combinations of demonstration data and qualitative knowledge about the data-generating process represented as a causal diagram.- The authors provide a graphical criterion that is complete for determining the feasibility of learning an imitating policy that mimics the expert’s performance.
- An efficient algorithm is introduced which finds an imitating policy, by exploiting quantitative knowledge contained in the observational data and the presence of surrogate endpoints.
- The authors propose a practical procedure for estimating such an imitating policy from observed trajectories of the expert’s demonstrations

基金

- The authors were partially supported by grants from NSF IIS-1704352 and IIS-1750807 (CAREER)

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