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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 401–450 of 2122 papers

TitleStatusHype
Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive ImitationCode0
Learning Memory Mechanisms for Decision Making through DemonstrationsCode0
Decoding fairness: a reinforcement learning perspectiveCode0
Learning human behaviors from motion capture by adversarial imitationCode0
Learning from Imperfect Demonstrations from Agents with Varying DynamicsCode0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement LearningCode0
Learning from Trajectories via Subgoal DiscoveryCode0
Backward Learning for Goal-Conditioned PoliciesCode0
Learning Latent Process from High-Dimensional Event Sequences via Efficient SamplingCode0
Deconfounding Imitation Learning with Variational InferenceCode0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
Learning Generalizable 3D Manipulation With 10 DemonstrationsCode0
Deep attention networks reveal the rules of collective motion in zebrafishCode0
Learning One-Shot Imitation from Humans without HumansCode0
Learning for Long-Horizon Planning via Neuro-Symbolic Abductive ImitationCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Deep Homography Prediction for Endoscopic Camera Motion Imitation LearningCode0
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative SamplingCode0
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality TeleoperationCode0
Learning Beam Search Policies via Imitation LearningCode0
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic SupervisorCode0
Learning Belief Representations for Imitation Learning in POMDPsCode0
Learning Calibratable Policies using Programmatic Style-ConsistencyCode0
BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoTCode0
Learning to Guide and to Be Guided in the Architect-Builder ProblemCode0
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic SimulationCode0
Learning from Mistakes via Cooperative Study Assistant for Large Language ModelsCode0
Curriculum-Based Imitation of Versatile SkillsCode0
Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert DemonstrationsCode0
Autoregressive Knowledge Distillation through Imitation LearningCode0
Iterative Document-level Information Extraction via Imitation LearningCode0
Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal RepresentationsCode0
Minimax Optimal Online Imitation Learning via Replay EstimationCode0
OIL-AD: An Anomaly Detection Framework for Sequential Decision SequencesCode0
Interactive Learning from Activity DescriptionCode0
Interactive Imitation Learning in State-SpaceCode0
Cross Domain Robot Imitation with Invariant RepresentationCode0
Interactive incremental learning of generalizable skills with local trajectory modulationCode0
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task HierarchyCode0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and InvestigationCode0
InfoGAIL: Interpretable Imitation Learning from Visual DemonstrationsCode0
Amplifying the Imitation Effect for Reinforcement Learning of UCAV's Mission ExecutionCode0
Domain Adaptive Imitation LearningCode0
Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse SkillsCode0
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex TasksCode0
Automatic Discovery of Interpretable Planning StrategiesCode0
Improving Policy Optimization with Generalist-Specialist LearningCode0
Automatic Discovery and Description of Human Planning StrategiesCode0
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