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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
Imitation learning for language generation from unaligned data—0
Imitation Learning for Neural Network Autopilot in Fixed-Wing Unmanned Aerial Systems—0
Imitation Learning for Non-Autoregressive Neural Machine Translation—0
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach—0
Imitation learning for structured prediction in natural language processing—0
Imitation Learning for Vision-based Lane Keeping Assistance—0
Imitation Learning from Imperfect Demonstration—0
Imitation Learning from MPC for Quadrupedal Multi-Gait Control—0
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
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement—0
Imitation Learning from Observation through Optimal Transport—0
Imitation Learning from Pixel Observations for Continuous Control—0
Imitation Learning from Video by Leveraging Proprioception—0
Imitation Learning from Visual Data with Multiple Intentions—0
Imitation Learning in Discounted Linear MDPs without exploration assumptions—0
Imitation Learning Inputting Image Feature to Each Layer of Neural Network—0
Imitation Learning of Correlated Policies in Stackelberg Games—0
Imitation Learning of Factored Multi-agent Reactive Models—0
Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification—0
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration—0
Imitation Learning of Robot Policies using Language, Vision and Motion—0
Imitation Learning: Progress, Taxonomies and Challenges—0
Imitation Learning via Focused Satisficing—0
Imitation Learning with Concurrent Actions in 3D Games—0
Imitation Learning with Precisely Labeled Human Demonstrations—0
Imitation Learning with Recurrent Neural Networks—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
IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimation—0
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making—0
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy—0
Implicit and Explicit Commonsense for Multi-sentence Video Captioning—0
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning—0
Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving—0
Improved Sample Complexity of Imitation Learning for Barrier Model Predictive Control—0
Improving Adversarial Text Generation by Modeling the Distant Future—0
Improving Generalization in Game Agents with Data Augmentation in Imitation Learning—0
Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations—0
Improving Learning from Demonstrations by Learning from Experience—0
Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback—0
Improving Retrospective Language Agents via Joint Policy Gradient Optimization—0
Improving Sequential Recommendation Consistency with Self-Supervised Imitation—0
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight—0
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition—0
Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation—0
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