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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 901–910 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
Evaluation Function Approximation for Scrabble—0
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement—0
Imitation Learning from Observation through Optimal Transport—0
Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots—0
Imitation Learning from Pixel Observations for Continuous Control—0
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning—0
A Simple Imitation Learning Method via Contrastive Regularization—0
ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy—0
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