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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 731–740 of 2122 papers

TitleStatusHype
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments—0
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models—0
Offline Imitation Learning with Variational Counterfactual ReasoningCode0
Blending Imitation and Reinforcement Learning for Robust Policy Improvement—0
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent—0
Imitation Learning from Observation through Optimal Transport—0
Interpretable Imitation Learning with Dynamic Causal Relations—0
Learning Decentralized Flocking Controllers with Spatio-Temporal Graph Neural Network—0
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem SolvingCode3
Adversarial Imitation Learning from Visual Observations using Latent InformationCode0
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