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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 651–675 of 2122 papers

TitleStatusHype
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Faster Reinforcement Learning with Expert State Sequences—0
Fast fixed-backbone protein sequence and rotamer design—0
Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baseline—0
FDPP: Fine-tune Diffusion Policy with Human Preference—0
Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface—0
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Feedback in Imitation Learning: The Three Regimes of Covariate Shift—0
Few-Shot Bayesian Imitation Learning with Logical Program Policies—0
Few-Shot In-Context Imitation Learning via Implicit Graph Alignment—0
Exponentially Weighted Imitation Learning for Batched Historical Data—0
Co-Imitation Learning without Expert Demonstration—0
Fight fire with fire: countering bad shortcuts in imitation learning with good shortcuts—0
Fighting Copycat Agents in Behavioral Cloning from Observation Histories—0
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming—0
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching—0
Gaze Training by Modulated Dropout Improves Imitation Learning—0
Find a Way Forward: a Language-Guided Semantic Map Navigator—0
Generalizable Imitation Learning from Observation via Inferring Goal Proximity—0
Finding Fallen Objects Via Asynchronous Audio-Visual Integration—0
Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback—0
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation, Demonstration, and Imitation—0
Exploring the use of deep learning in task-flexible ILC—0
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