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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 771–780 of 2122 papers

TitleStatusHype
Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning—0
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations—0
Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets—0
Adaptive Adversarial Imitation Learning—0
Delayed Reinforcement Learning by Imitation—0
Deep Reinforcement Learning with Mixed Convolutional Network—0
Amortized nonmyopic active search via deep imitation learning—0
Domain-adapted Learning and Imitation: DRL for Power Arbitrage—0
Deep Reinforcement Learning for Personalized Search Story Recommendation—0
Behavioral Cloning via Search in Video PreTraining Latent Space—0
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