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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 1701–1725 of 2122 papers

TitleStatusHype
Human AI interaction loop training: New approach for interactive reinforcement learning—0
PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving—0
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate—0
A Geometric Perspective on Visual Imitation Learning—0
MPC-guided Imitation Learning of Neural Network Policies for the Artificial PancreasCode0
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift—0
How Do We Move: Modeling Human Movement with System Dynamics—0
Efficiently Guiding Imitation Learning Agents with Human Gaze—0
Provably Efficient Third-Person Imitation from Offline Observation—0
Scalable Multi-Task Imitation Learning with Autonomous Improvement—0
Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms—0
Provable Representation Learning for Imitation Learning via Bi-level Optimization—0
Support-weighted Adversarial Imitation Learning—0
Follow the Neurally-Perturbed Leader for Adversarial TrainingCode0
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement LearningCode0
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Combating False Negatives in Adversarial Imitation Learning—0
Deep Reinforcement Learning for Autonomous Driving: A Survey—0
Preventing Imitation Learning with Adversarial Policy Ensembles—0
Domain-Adversarial and Conditional State Space Model for Imitation Learning—0
Exploration Based Language Learning for Text-Based Games—0
A Probabilistic Framework for Imitating Human Race Driver Behavior—0
Nested-Wasserstein Self-Imitation Learning for Sequence Generation—0
Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances—0
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