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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 1251–1260 of 2122 papers

TitleStatusHype
Supervised Fine-Tuning as Inverse Reinforcement Learning—0
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)—0
Support-guided Adversarial Imitation Learning—0
Support-weighted Adversarial Imitation Learning—0
Swarm Behavior Cloning—0
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation—0
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies—0
Synthesizing Physical Character-Scene Interactions—0
Synthesizing Programmatic Policies that Inductively Generalize—0
Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning—0
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