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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 1051–1100 of 2122 papers

TitleStatusHype
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
End-to-End Stable Imitation Learning via Autonomous Neural Dynamic Policies—0
On the Correspondence between Compositionality and Imitation in Emergent Neural Communication—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
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters—0
Learning Environment for the Air Domain (LEAD)—0
Distance Weighted Supervised Learning for Offline Interaction DataCode0
Programmatically Grounded, Compositionally Generalizable Robotic Manipulation—0
Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach—0
Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware—0
Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive ImitationCode0
Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets—0
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated EnvironmentsCode0
Affordances from Human Videos as a Versatile Representation for Robotics—0
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed—0
Reward-free Policy Imitation Learning for Conversational Search—0
Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning—0
Car-Following Models: A Multidisciplinary Review—0
Curriculum-Based Imitation of Versatile SkillsCode0
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal—0
CRISP: Curriculum Inducing Primitive Informed Subgoal Prediction for Hierarchical Reinforcement Learning—0
Learning Robot Manipulation from Cross-Morphology DemonstrationCode0
End-to-end Manipulator Calligraphy Planning via Variational Imitation Learning—0
ENTL: Embodied Navigation Trajectory Learner—0
Quantum Imitation Learning—0
Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation—0
Generative Adversarial Neuroevolution for Control Behaviour ImitationCode0
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from ObservationsCode0
Specification-Guided Data Aggregation for Semantically Aware Imitation Learning—0
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costsCode0
Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation—0
Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse SkillsCode0
Exploring the use of deep learning in task-flexible ILC—0
Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football—0
Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning—0
Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks—0
Penalty-Based Imitation Learning With Cross Semantics Generation Sensor Fusion for Autonomous Driving—0
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale—0
Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum—0
Sample-efficient Adversarial Imitation Learning—0
Implicit and Explicit Commonsense for Multi-sentence Video Captioning—0
Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation—0
ConBaT: Control Barrier Transformer for Safe Policy Learning—0
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching—0
Interactive Text Generation—0
MEGA-DAgger: Imitation Learning with Multiple Imperfect ExpertsCode0
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