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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 701–750 of 2122 papers

TitleStatusHype
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation—0
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning—0
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving—0
GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback—0
GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts—0
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation—0
Exploring the use of deep learning in task-flexible ILC—0
Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks—0
Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation—0
Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL)—0
Exploring the trade off between human driving imitation and safety for traffic simulation—0
Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning—0
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning—0
Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control—0
Generalizable Imitation Learning from Observation via Inferring Goal Proximity—0
Generalizable Imitation Learning Through Pre-Trained Representations—0
Generalization Capability for Imitation Learning—0
Co-Imitation: Learning Design and Behaviour by Imitation—0
Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective—0
A Strong Baseline for Batch Imitation Learning—0
Generalized Robot Learning Framework—0
Generalizing to New Tasks via One-Shot Compositional Subgoals—0
CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics—0
Generating Personas for Games with Multimodal Adversarial Imitation Learning—0
Generating Physically Realistic and Directable Human Motions from Multi-Modal Inputs—0
Informed Sampling for Diversity in Concept-to-Text NLG—0
GymFG: A Framework with a Gym Interface for FlightGear—0
Generating stable molecules using imitation and reinforcement learning—0
Auto-Encoding Inverse Reinforcement Learning—0
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning—0
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate—0
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate—0
Generative Adversarial Imitation Learning for Empathy-based AI—0
Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments—0
Action-Free Reasoning for Policy Generalization—0
Exploration Based Language Learning for Text-Based Games—0
Explaining Imitation Learning through Frames—0
Generative predecessor models for sample-efficient imitation learning—0
Generic Oracles for Structured Prediction—0
Genetic Imitation Learning by Reward Extrapolation—0
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
CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity—0
Gesture2Path: Imitation Learning for Gesture-aware Navigation—0
Get Back Here: Robust Imitation by Return-to-Distribution Planning—0
GHIL-Glue: Hierarchical Control with Filtered Subgoal Images—0
CMR-Agent: Learning a Cross-Modal Agent for Iterative Image-to-Point Cloud Registration—0
H2O: A Benchmark for Visual Human-human Object Handover Analysis—0
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods—0
Explaining Fast Improvement in Online Imitation Learning—0
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