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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 901–950 of 2122 papers

TitleStatusHype
Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation—0
Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach—0
CLUZH at SIGMORPHON 2021 Shared Task on Multilingual Grapheme-to-Phoneme Conversion: Variations on a Baseline—0
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement—0
Imitation Learning from Observation through Optimal Transport—0
Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation—0
Imitation Learning from Pixel Observations for Continuous Control—0
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning—0
CLUZH at SIGMORPHON 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion—0
Imitation Learning from Video by Leveraging Proprioception—0
Event Extraction with Generative Adversarial Imitation Learning—0
Imitation Learning in Discounted Linear MDPs without exploration assumptions—0
Imitation Learning Inputting Image Feature to Each Layer of Neural Network—0
Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation—0
Imitation Learning of Correlated Policies in Stackelberg Games—0
Imitation Learning of Factored Multi-agent Reactive Models—0
Evaluation metrics for behaviour modeling—0
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration—0
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
Imitation Learning of Robot Policies using Language, Vision and Motion—0
Imitation Learning: Progress, Taxonomies and Challenges—0
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models—0
Imitation Learning via Focused Satisficing—0
Adversarial Imitation Learning via Boosting—0
Evaluation Function Approximation for Scrabble—0
Imitation Learning with Concurrent Actions in 3D Games—0
Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots—0
A Simple Imitation Learning Method via Contrastive Regularization—0
ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy—0
On the Sample Complexity of Stability Constrained Imitation Learning—0
Adversarial Imitation Learning On Aggregated Data—0
Imitation-Projected Programmatic Reinforcement Learning—0
Imitation-regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning—0
Imitation with Neural Density Models—0
Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation—0
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments—0
Imit Diff: Semantics Guided Diffusion Transformer with Dual Resolution Fusion for Imitation Learning—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning—0
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making—0
Error Bounds of Imitating Policies and Environments—0
Implicit and Explicit Commonsense for Multi-sentence Video Captioning—0
Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation—0
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning—0
Ergodic Generative Flows—0
Improved Sample Complexity of Imitation Learning for Barrier Model Predictive Control—0
Improving Adversarial Text Generation by Modeling the Distant Future—0
CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations—0
Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning—0
Interactive Agent Modeling by Learning to Probe—0
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