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Imitation Learning

Imitation Learning is a framework for learning a behavior policy from demonstrations. Usually, demonstrations are presented in the form of state-action trajectories, with each pair indicating the action to take at the state being visited. In order to learn the behavior policy, the demonstrated actions are usually utilized in two ways. The first, known as Behavior Cloning (BC), treats the action as the target label for each state, and then learns a generalized mapping from states to actions in a supervised manner. Another way, known as Inverse Reinforcement Learning (IRL), views the demonstrated actions as a sequence of decisions, and aims at finding a reward/cost function under which the demonstrated decisions are optimal.

Finally, a newer methodology, Inverse Q-Learning aims at directly learning Q-functions from expert data, implicitly representing rewards, under which the optimal policy can be given as a Boltzmann distribution similar to soft Q-learning

Source: Learning to Imitate

Papers

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Showing 1101–1110 of 2122 papers

TitleStatusHype
Reinforcement and Imitation Learning via Interactive No-Regret Learning—0
Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer—0
Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning—0
Reinforcement Learning via Implicit Imitation Guidance—0
Reinforcement Learning without Human Feedback for Last Mile Fine-Tuning of Large Language Models—0
Reinforcement Routing on Proximity Graph for Efficient Recommendation—0
Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones—0
Relational Mimic for Visual Adversarial Imitation Learning—0
Reparameterized Variational Divergence Minimization for Stable Imitation—0
Repeated Inverse Reinforcement Learning—0
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