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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 1801–1850 of 2122 papers

TitleStatusHype
CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous DrivingCode0
Distance Weighted Supervised Learning for Offline Interaction DataCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning for Long-Horizon Planning via Neuro-Symbolic Abductive ImitationCode0
Online Adaptation for Enhancing Imitation Learning PoliciesCode0
Reward-Conditioned PoliciesCode0
Learning Belief Representations for Imitation Learning in POMDPsCode0
Learning Beam Search Policies via Imitation LearningCode0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
Learning from Imperfect Demonstrations from Agents with Varying DynamicsCode0
Online Baum-Welch algorithm for Hierarchical Imitation LearningCode0
Online Cascade Learning for Efficient Inference over StreamsCode0
Learning from Trajectories via Subgoal DiscoveryCode0
Learning Generalizable 3D Manipulation With 10 DemonstrationsCode0
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic GraspingCode0
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation LearningCode0
Contractive Dynamical Imitation Policies for Efficient Out-of-Sample RecoveryCode0
Reward learning from human preferences and demonstrations in AtariCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Learning human behaviors from motion capture by adversarial imitationCode0
Learning from Mistakes via Cooperative Study Assistant for Large Language ModelsCode0
Learning Latent Process from High-Dimensional Event Sequences via Efficient SamplingCode0
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous DrivingCode0
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image TranslationCode0
Learning Memory Mechanisms for Decision Making through DemonstrationsCode0
Simitate: A Hybrid Imitation Learning BenchmarkCode0
Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learningCode0
Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation LearningCode0
Simulation of robot swarms for learning communication-aware coordinationCode0
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic SimulationCode0
Learning Robot Manipulation from Cross-Morphology DemonstrationCode0
On the stability analysis of deep neural network representations of an optimal state-feedbackCode0
Adversarial Moment-Matching Distillation of Large Language ModelsCode0
Learning non-Markovian Decision-Making from State-only SequencesCode0
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated EnvironmentsCode0
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in ClutterCode0
Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert DemonstrationsCode0
Learning One-Shot Imitation from Humans without HumansCode0
RIZE: Regularized Imitation Learning via Distributional Reinforcement LearningCode0
Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement LearningCode0
RLBench: The Robot Learning Benchmark & Learning EnvironmentCode0
Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation LearningCode0
Guiding Policies with Language via Meta-LearningCode0
Optimal Decision Tree Policies for Markov Decision ProcessesCode0
Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive ImitationCode0
Differentiable MPC for End-to-end Planning and ControlCode0
Guiding Attention in End-to-End Driving ModelsCode0
Text Editing as Imitation GameCode0
Iterative Document-level Information Extraction via Imitation LearningCode0
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative SamplingCode0
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