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	48.44%
Reinforcement Learning (RL)	12	18.75%
Continuous Control	4	6.25%
Autonomous Driving	2	3.13%
Autonomous Navigation	2	3.13%
Navigate	1	1.56%
Decoder	1	1.56%
Quantization	1	1.56%
Denoising	1	1.56%

Usage Over Time

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

Components

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🤖 No Components Found	You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

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Adversarial Training