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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 676–700 of 2122 papers

TitleStatusHype
Co-Imitation Learning without Expert Demonstration—0
FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control—0
Fixing exposure bias with imitation learning needs powerful oracles—0
Generic Oracles for Structured Prediction—0
Flatland-RL : Multi-Agent Reinforcement Learning on Trains—0
FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation—0
Flexible and Efficient Long-Range Planning Through Curious Exploration—0
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation Demonstration and Imitation—0
FlowHFT: Imitation Learning via Flow Matching Policy for Optimal High-Frequency Trading under Diverse Market Conditions—0
FlowOE: Imitation Learning with Flow Policy from Ensemble RL Experts for Optimal Execution under Heston Volatility and Concave Market Impacts—0
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment—0
Goal-Driven Imitation Learning from Observation by Inferring Goal Proximity—0
Exploring the use of deep learning in task-flexible ILC—0
A Training-Free Framework for Precise Mobile Manipulation of Small Everyday Objects—0
Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks—0
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal—0
Exploring the trade off between human driving imitation and safety for traffic simulation—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models—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
Fully General Online Imitation Learning—0
FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulation—0
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate—0
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