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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 601–650 of 2122 papers

TitleStatusHype
Interpretable Generative Adversarial Imitation Learning—0
Single-Reset Divide & Conquer Imitation Learning—0
Hybrid Inverse Reinforcement LearningCode1
LLM-driven Imitation of Subrational Behavior : Illusion or Reality?—0
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill—0
A Competition Winning Deep Reinforcement Learning Agent in microRTSCode1
Policy Improvement using Language Feedback ModelsCode0
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive LossCode1
DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation LearningCode1
OIL-AD: An Anomaly Detection Framework for Sequential Decision SequencesCode0
Online Cascade Learning for Efficient Inference over StreamsCode0
AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based PoliciesCode2
SEABO: A Simple Search-Based Method for Offline Imitation LearningCode1
CAMBranch: Contrastive Learning with Augmented MILPs for Branching—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
Accelerating Inverse Reinforcement Learning with Expert Bootstrapping—0
Inverse Reinforcement Learning by Estimating Expertise of DemonstratorsCode0
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation LearningCode0
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient UpdateCode1
LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory OptimizationCode1
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
Context-Former: Stitching via Latent Conditioned Sequence Modeling—0
Emergence of cooperation under punishment: A reinforcement learning perspective—0
Zero-shot Imitation Policy via Search in Demonstration Dataset—0
Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation LearningCode0
Steering Language Models with Game-Theoretic SolversCode9
Multi-Object Navigation in real environments using hybrid policies—0
Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations—0
Visual Imitation Learning with Calibrated Contrastive Representation—0
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance—0
LangProp: A code optimization framework using Large Language Models applied to drivingCode2
Offline Imitation Learning by Controlling the Effective Planning Horizon—0
Imitation Learning Inputting Image Feature to Each Layer of Neural Network—0
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation—0
AgentMixer: Multi-Agent Correlated Policy Factorization—0
Learning Stable Koopman Embeddings for Identification and Control—0
Robotic Imitation of Human Actions—0
Multi-task real-robot data with gaze attention for dual-arm fine manipulation—0
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy—0
LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage ControlCode1
Robust Imitation Learning for Automated Game Testing—0
Behavioural Cloning in VizDoom—0
Autonomous Navigation in Complex Environments—0
Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach—0
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation—0
SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA datasetCode1
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation Demonstration and Imitation—0
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation, Demonstration, and Imitation—0
Agnostic Interactive Imitation Learning: New Theory and Practical AlgorithmsCode0
An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data—0
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