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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 1851–1875 of 2122 papers

TitleStatusHype
ENTL: Embodied Navigation Trajectory Learner—0
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation—0
EPR-GAIL: An EPR-Enhanced Hierarchical Imitation Learning Framework to Simulate Complex User Consumption Behaviors—0
EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning—0
Ergodic Generative Flows—0
Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation—0
Error Bounds of Imitating Policies and Environments—0
Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning—0
ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy—0
Evaluation Function Approximation for Scrabble—0
Evaluation metrics for behaviour modeling—0
Event Extraction with Generative Adversarial Imitation Learning—0
Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a Demonstrator—0
Evolution of cooperation in the public goods game with Q-learning—0
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation—0
ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers—0
Imitation Learning from Pixel-Level Demonstrations by HashReward—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring—0
Explaining Autonomous Driving by Learning End-to-End Visual Attention—0
Explaining Fast Improvement in Online Imitation Learning—0
Explaining Imitation Learning through Frames—0
Exploration Based Language Learning for Text-Based Games—0
Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective—0
Exploring the trade off between human driving imitation and safety for traffic simulation—0
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