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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 1891–1900 of 2122 papers

TitleStatusHype
Fast Policy Learning through Imitation and Reinforcement—0
FDPP: Fine-tune Diffusion Policy with Human Preference—0
Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface—0
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Feedback in Imitation Learning: The Three Regimes of Covariate Shift—0
Few-Shot Bayesian Imitation Learning with Logical Program Policies—0
Few-Shot In-Context Imitation Learning via Implicit Graph Alignment—0
f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning—0
Fight fire with fire: countering bad shortcuts in imitation learning with good shortcuts—0
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