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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 651–675 of 2122 papers

TitleStatusHype
Active Third-Person Imitation Learning—0
A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement LearningCode0
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
LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop EnvironmentsCode0
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
DiffAIL: Diffusion Adversarial Imitation LearningCode1
Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing—0
Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning—0
Language-Conditioned Semantic Search-Based Policy for Robotic Manipulation Tasks—0
Backward Learning for Goal-Conditioned PoliciesCode0
Understanding Representations Pretrained with Auxiliary Losses for Embodied Agent Planning—0
Visual Hindsight Self-Imitation Learning for Interactive Navigation—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games—0
Domain Adaptive Imitation Learning with Visual Observation—0
DeformGS: Scene Flow in Highly Deformable Scenes for Deformable Object Manipulation—0
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent—0
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges—0
Toward a Surgeon-in-the-Loop Ophthalmic Robotic Apprentice using Reinforcement and Imitation LearningCode0
Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorldCode1
Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks—0
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