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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 851–875 of 2122 papers

TitleStatusHype
Align Your Intents: Offline Imitation Learning via Optimal Transport—0
Tiny Reinforcement Learning for Quadruped Locomotion using Decision TransformersCode0
Interpretable Generative Adversarial Imitation Learning—0
Single-Reset Divide & Conquer Imitation Learning—0
LLM-driven Imitation of Subrational Behavior : Illusion or Reality?—0
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill—0
Policy Improvement using Language Feedback ModelsCode0
Online Cascade Learning for Efficient Inference over StreamsCode0
OIL-AD: An Anomaly Detection Framework for Sequential Decision SequencesCode0
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
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
Zero-shot Imitation Policy via Search in Demonstration Dataset—0
Context-Former: Stitching via Latent Conditioned Sequence Modeling—0
Emergence of cooperation under punishment: A reinforcement learning perspective—0
Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation LearningCode0
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
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance—0
Visual Imitation Learning with Calibrated Contrastive Representation—0
Imitation Learning Inputting Image Feature to Each Layer of Neural Network—0
Offline Imitation Learning by Controlling the Effective Planning Horizon—0
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