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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 901–925 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
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement—0
Imitation Learning from Observation through Optimal Transport—0
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models—0
Imitation Learning from Pixel Observations for Continuous Control—0
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning—0
Adversarial Imitation Learning via Boosting—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
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 Function Approximation for Scrabble—0
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration—0
Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots—0
Imitation Learning of Robot Policies using Language, Vision and Motion—0
Imitation Learning: Progress, Taxonomies and Challenges—0
A Simple Imitation Learning Method via Contrastive Regularization—0
Imitation Learning via Focused Satisficing—0
ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy—0
On the Sample Complexity of Stability Constrained Imitation Learning—0
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