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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 2051–2100 of 2122 papers

TitleStatusHype
Towards Improving Exploration in Self-Imitation Learning using Intrinsic MotivationCode0
Reactive and Safe Road User Simulations using Neural Barrier CertificatesCode0
Episodic Self-Imitation Learning with HindsightCode0
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context TranslationCode0
Agnostic Interactive Imitation Learning: New Theory and Practical AlgorithmsCode0
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement LearningCode0
Multi-task Maximum Entropy Inverse Reinforcement LearningCode0
Decoding fairness: a reinforcement learning perspectiveCode0
Enhancing Robot Learning through Learned Human-Attention Feature MapsCode0
Muscle-actuated Human Simulation and ControlCode0
Imitating Driver Behavior with Generative Adversarial NetworksCode0
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline DataCode0
Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation LearningCode0
Supervise Thyself: Examining Self-Supervised Representations in Interactive EnvironmentsCode0
A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement LearningCode0
Bayesian Robust Optimization for Imitation LearningCode0
Curriculum-Based Imitation of Versatile SkillsCode0
Causal Confusion in Imitation LearningCode0
Imitating Cost-Constrained Behaviors in Reinforcement LearningCode0
Cross Domain Robot Imitation with Invariant RepresentationCode0
Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation LearningCode0
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?Code0
VISITRON: Visual Semantics-Aligned Interactively Trained Object-NavigatorCode0
Adversarial Imitation Learning from Visual Observations using Latent InformationCode0
Neural Modular Control for Embodied Question AnsweringCode0
Unsupervised Reward Shaping for a Robotic Sequential Picking Task from Visual Observations in a Logistics ScenarioCode0
Self-Imitation LearningCode0
Bayesian Nonparametrics for Offline Skill DiscoveryCode0
Neural Task Synthesis for Visual ProgrammingCode0
NeuroCERIL: Robotic Imitation Learning via Hierarchical Cause-Effect Reasoning in Programmable Attractor Neural NetworksCode0
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
Learning to Score Behaviors for Guided Policy OptimizationCode0
Self-Imitation Learning for Robot Tasks with Sparse and Delayed RewardsCode0
Self-Imitation Learning of Locomotion Movements through Termination CurriculumCode0
Active Multi-task Policy Fine-tuningCode0
End-to-end Sketch-Guided Path Planning through Imitation Learning for Autonomous Mobile RobotsCode0
Reinforcement and Imitation Learning for Diverse Visuomotor SkillsCode0
IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human SupervisorsCode0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement LearningCode0
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learningCode0
Non-Adversarial Imitation Learning and its Connections to Adversarial MethodsCode0
No Need for Interactions: Robust Model-Based Imitation Learning using Neural ODECode0
Non-Monotonic Sequential Text GenerationCode0
Non-Parallel Text Style Transfer with Self-Parallel SupervisionCode0
IALE: Imitating Active Learner EnsemblesCode0
End-to-end Driving via Conditional Imitation LearningCode0
Hybrid system identification using switching density networksCode0
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware PerspectiveCode0
Domain Adaptive Imitation LearningCode0
Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-TimeCode0
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