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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 1351–1375 of 2122 papers

TitleStatusHype
Adversarial Mixture Density Networks: Learning to Drive Safely from Collision DataCode0
Imitation by Predicting Observations—0
The MineRL BASALT Competition on Learning from Human Feedback—0
On the Benefits of Inducing Local Lipschitzness for Robust Generative Adversarial Imitation Learning—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Improving Sequential Recommendation Consistency with Self-Supervised Imitation—0
Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View—0
Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural NetworksCode1
Imitation Learning: Progress, Taxonomies and Challenges—0
IQ-Learn: Inverse soft-Q Learning for ImitationCode1
On Generalization of Adversarial Imitation Learning and Beyond—0
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning—0
CRIL: Continual Robot Imitation Learning via Generative and Prediction ModelCode1
Automatic Curricula via Expert Demonstrations—0
Causal Navigation by Continuous-time Neural NetworksCode0
Reinforcement Learning as One Big Sequence Modeling ProblemCode1
SparseDice: Imitation Learning for Temporally Sparse Data via Regularization—0
Solving Graph-based Public Good Games with Tree Search and Imitation LearningCode0
Policy Gradient Bayesian Robust Optimization for Imitation Learning—0
Keyframe-Focused Visual Imitation Learning—0
Adversarial Option-Aware Hierarchical Imitation LearningCode1
Differentiable Robust LQR Layers—0
Offline Inverse Reinforcement Learning—0
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint—0
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning—0
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