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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 876–900 of 2122 papers

TitleStatusHype
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation—0
Robotic Imitation of Human Actions—0
AgentMixer: Multi-Agent Correlated Policy Factorization—0
Learning Stable Koopman Embeddings for Identification and Control—0
Multi-task real-robot data with gaze attention for dual-arm fine manipulation—0
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy—0
Robust Imitation Learning for Automated Game Testing—0
Behavioural Cloning in VizDoom—0
Autonomous Navigation in Complex Environments—0
Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach—0
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation—0
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation Demonstration and Imitation—0
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation, Demonstration, and Imitation—0
Agnostic Interactive Imitation Learning: New Theory and Practical AlgorithmsCode0
Active Third-Person Imitation Learning—0
An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data—0
A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement LearningCode0
LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop EnvironmentsCode0
Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation—0
On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation—0
Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective—0
GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction EstimationCode0
Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects—0
Neural Differentiable Integral Control Barrier Functions for Unknown Nonlinear Systems with Input Constraints—0
Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing—0
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