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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
GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction EstimationCode0
IALE: Imitating Active Learner EnsemblesCode0
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex TasksCode0
Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation, Transferable Reward Recovery and Algebraic Equilibrium ProofCode0
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning—0
Bridging the Sim-to-real Gap: A Control Framework for Imitation Learning of Model Predictive Control—0
Divide and Repair: Using Options to Improve Performance of Imitation Learning Against Adversarial Demonstrations—0
Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms—0
An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models—0
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning—0
Offline Diversity Maximization Under Imitation Constraints—0
Diverse Imitation Learning via Self-Organizing Generative Models—0
Diverse Imitation Learning via Self-OrganizingGenerative Models—0
Bridging the Imitation Gap by Adaptive Insubordination—0
An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation—0
DITTO: Offline Imitation Learning with World Models—0
Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks—0
Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation—0
Distributionally Robust Imitation Learning—0
Distributional Decision Transformer for Hindsight Information Matching—0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Distilling Realizable Students from Unrealizable Teachers—0
Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale—0
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