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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 101–150 of 2122 papers

TitleStatusHype
Everyone Deserves A Reward: Learning Customized Human PreferencesCode1
Energy-Based Imitation LearningCode1
Generalization Guarantees for Imitation LearningCode1
EvIL: Evolution Strategies for Generalisable Imitation LearningCode1
Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid LocomotionCode1
Explorative Imitation Learning: A Path Signature Approach for Continuous EnvironmentsCode1
DivScene: Benchmarking LVLMs for Object Navigation with Diverse Scenes and ObjectsCode1
f-IRL: Inverse Reinforcement Learning via State Marginal MatchingCode1
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot ManipulationCode1
Domain-Robust Visual Imitation Learning with Mutual Information ConstraintsCode1
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
An Adversarial Imitation Click Model for Information RetrievalCode1
Generative Adversarial Imitation LearningCode1
Globally Stable Neural Imitation PoliciesCode1
Goal-Conditioned Imitation Learning using Score-based Diffusion PoliciesCode1
Go-Explore: a New Approach for Hard-Exploration ProblemsCode1
An Empirical Investigation of Representation Learning for ImitationCode1
Guiding Data Collection via Factored Scaling CurvesCode1
A System for Morphology-Task Generalization via Unified Representation and Behavior DistillationCode1
Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban EnvironmentsCode1
How To Guide Your Learner: Imitation Learning with Active Adaptive Expert InvolvementCode1
An Imitation Game for Learning Semantic Parsers from User InteractionCode1
A Competition Winning Deep Reinforcement Learning Agent in microRTSCode1
Hybrid Inverse Reinforcement LearningCode1
A Divergence Minimization Perspective on Imitation Learning MethodsCode1
IGDrivSim: A Benchmark for the Imitation Gap in Autonomous DrivingCode1
Discriminator-Weighted Offline Imitation Learning from Suboptimal DemonstrationsCode1
DotaMath: Decomposition of Thought with Code Assistance and Self-correction for Mathematical ReasoningCode1
A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character ControlCode1
Disagreement-Regularized Imitation LearningCode1
DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation LearningCode1
Diffusing States and Matching Scores: A New Framework for Imitation LearningCode1
Dynamic Conditional Imitation Learning for Autonomous DrivingCode1
DexMV: Imitation Learning for Dexterous Manipulation from Human VideosCode1
DERAIL: Diagnostic Environments for Reward And Imitation LearningCode1
Active Imitation Learning with Noisy GuidanceCode1
Adversarial Soft Advantage Fitting: Imitation Learning without Policy OptimizationCode1
DEMO: Reframing Dialogue Interaction with Fine-grained Element ModelingCode1
Adversarial Option-Aware Hierarchical Imitation LearningCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous DrivingCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Deep Imitation Learning for Bimanual Robotic ManipulationCode1
DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object Manipulation via Preference-based Action AlignmentCode1
DiffAIL: Diffusion Adversarial Imitation LearningCode1
EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical ConstraintsCode1
Curriculum Offline Imitation LearningCode1
Curricular Subgoals for Inverse Reinforcement LearningCode1
A Bayesian Approach to Robust Inverse Reinforcement LearningCode1
Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised LearningCode1
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