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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 851–900 of 2122 papers

TitleStatusHype
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters—0
A Coupled Flow Approach to Imitation LearningCode1
Learning Environment for the Air Domain (LEAD)—0
Learning to Extrapolate: A Transductive ApproachCode1
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
Self-Supervised Adversarial Imitation LearningCode1
Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive ImitationCode0
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated EnvironmentsCode0
Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets—0
Reward-free Policy Imitation Learning for Conversational Search—0
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed—0
Affordances from Human Videos as a Versatile Representation for Robotics—0
Car-Following Models: A Multidisciplinary Review—0
Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning—0
Curriculum-Based Imitation of Versatile SkillsCode0
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal—0
Learning Robot Manipulation from Cross-Morphology DemonstrationCode0
CRISP: Curriculum Inducing Primitive Informed Subgoal Prediction for Hierarchical Reinforcement Learning—0
End-to-end Manipulator Calligraphy Planning via Variational Imitation Learning—0
Goal-Conditioned Imitation Learning using Score-based Diffusion PoliciesCode1
ENTL: Embodied Navigation Trajectory Learner—0
Quantum Imitation Learning—0
Generative Adversarial Neuroevolution for Control Behaviour ImitationCode0
Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation—0
Chain-of-Thought Predictive ControlCode1
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from ObservationsCode0
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costsCode0
Specification-Guided Data Aggregation for Semantically Aware Imitation Learning—0
Improving Code Generation by Training with Natural Language FeedbackCode1
Training Language Models with Language Feedback at ScaleCode1
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
Inverse Reinforcement Learning without Reinforcement LearningCode1
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
Optimal Transport for Offline Imitation LearningCode1
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
Implicit and Explicit Commonsense for Multi-sentence Video Captioning—0
Sample-efficient Adversarial Imitation Learning—0
ConBaT: Control Barrier Transformer for Safe Policy Learning—0
Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation—0
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching—0
How To Guide Your Learner: Imitation Learning with Active Adaptive Expert InvolvementCode1
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