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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 826–850 of 2122 papers

TitleStatusHype
PAGAR: Taming Reward Misalignment in Inverse Reinforcement Learning-Based Imitation Learning with Protagonist Antagonist Guided Adversarial Reward—0
Preference-grounded Token-level Guidance for Language Model Fine-tuningCode1
Thought Cloning: Learning to Think while Acting by Imitating Human ThinkingCode2
LIV: Language-Image Representations and Rewards for Robotic ControlCode1
Causal Imitability Under Context-Specific Independence Relations—0
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?Code0
GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts—0
Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data—0
Neural Task Synthesis for Visual ProgrammingCode0
How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers—0
Emergent Agentic Transformer from Chain of Hindsight Experience—0
Coherent Soft Imitation LearningCode1
Imitating Task and Motion Planning with Visuomotor Transformers—0
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models—0
Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles—0
Learning from Mistakes via Cooperative Study Assistant for Large Language ModelsCode0
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Multi-task Hierarchical Adversarial Inverse Reinforcement LearningCode1
On the Correspondence between Compositionality and Imitation in Emergent Neural Communication—0
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
End-to-End Stable Imitation Learning via Autonomous Neural Dynamic Policies—0
Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization—0
An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation—0
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization—0
Get Back Here: Robust Imitation by Return-to-Distribution Planning—0
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