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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 1251–1300 of 2122 papers

TitleStatusHype
Off-policy Imitation Learning from Visual Inputs—0
LILA: Language-Informed Latent ActionsCode1
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement LearningCode1
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies—0
Curriculum Offline Imitation LearningCode1
Smooth Imitation Learning via Smooth Costs and Smooth Policies—0
Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations—0
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner—0
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data—0
Object-Aware Regularization for Addressing Causal Confusion in Imitation LearningCode1
Confidence-Aware Imitation Learning from Demonstrations with Varying OptimalityCode1
Towards More Generalizable One-shot Visual Imitation Learning—0
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information—0
Sequential Voting with Relational Box Fields for Active Object DetectionCode1
Periodic DMP formulation for Quaternion Trajectories—0
Continuous Control with Action Quantization from Demonstrations—0
SS-MAIL: Self-Supervised Multi-Agent Imitation Learning—0
Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments—0
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning—0
FILM: Following Instructions in Language with Modular MethodsCode1
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement LearningCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Reinforcement Learning In Two Player Zero Sum Simultaneous Action GamesCode0
Leveraging Experience in Lazy Search—0
Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving—0
Cross-Domain Imitation Learning via Optimal TransportCode1
Goal-Directed Design Agents: Integrating Visual Imitation with One-Step Lookahead Optimization for Generative Design—0
Procedure Planning in Instructional Videos via Contextual Modeling and Model-based Policy Learning—0
A Critique of Strictly Batch Imitation Learning—0
Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion ExtractionCode0
Fast fixed-backbone protein sequence and rotamer design—0
State-Only Imitation Learning by Trajectory Distribution Matching—0
Lagrangian Generative Adversarial Imitation Learning with Safety—0
Learning the Representation of Behavior Styles with Imitation Learning—0
Transferring Hierarchical Structure with Dual Meta Imitation Learning—0
Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization—0
Emergent Communication at ScaleCode1
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning—0
Auto-Encoding Inverse Reinforcement Learning—0
Lagrangian Method for Episodic Learning—0
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow—0
Imitation Learning from Pixel Observations for Continuous Control—0
What Would the Expert do()?: Causal Imitation Learning—0
Fight fire with fire: countering bad shortcuts in imitation learning with good shortcuts—0
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations—0
Meta-Imitation Learning by Watching Video Demonstrations—0
Benchmarking Sample Selection Strategies for Batch Reinforcement Learning—0
CrowdPlay: Crowdsourcing human demonstration data for offline learning in Atari games—0
Language Model Pre-training Improves Generalization in Policy Learning—0
Distributional Decision Transformer for Hindsight Information Matching—0
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