Generative Adversarial Imitation Learning

Introduced by Ho et al. in Generative Adversarial Imitation Learning

Generative Adversarial Imitation Learning presents a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning.

Source: Generative Adversarial Imitation Learning

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Imitation Learning	31	49.21%
Reinforcement Learning (RL)	12	19.05%
Continuous Control	4	6.35%
Autonomous Driving	2	3.17%
Autonomous Navigation	2	3.17%
Navigate	1	1.59%
Quantization	1	1.59%
Denoising	1	1.59%
D4RL	1	1.59%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

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Adversarial Training