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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 976–1000 of 2122 papers

TitleStatusHype
Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control—0
Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Model—0
InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation—0
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps—0
Interactive Agent Modeling by Learning to Probe—0
A Simple Imitation Learning Method via Contrastive Regularization—0
Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey—0
ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy—0
Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets—0
On the Sample Complexity of Stability Constrained Imitation Learning—0
Interactive Text Generation—0
Adversarial Imitation Learning On Aggregated Data—0
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling—0
Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning—0
IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning—0
Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning—0
Error Bounds of Imitating Policies and Environments—0
Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation—0
Ergodic Generative Flows—0
Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory—0
Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning—0
Inverse Rational Control: Inferring What You Think from How You Forage—0
Distilling Realizable Students from Unrealizable Teachers—0
Distributional Decision Transformer for Hindsight Information Matching—0
CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations—0
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