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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 651–700 of 2122 papers

TitleStatusHype
Extracting Relations between Non-Standard Entities using Distant Supervision and Imitation Learning—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
Extracting Contact and Motion from Manipulation Videos—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
Collaborating Action by Action: A Multi-agent LLM Framework for Embodied Reasoning—0
Causal Confusion and Reward Misidentification in Preference-Based Reward Learning—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
Extending Multilingual Machine Translation through Imitation Learning—0
Find a Way Forward: a Language-Guided Semantic Map Navigator—0
Expressive Whole-Body Control for Humanoid Robots—0
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy—0
Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback—0
Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baseline—0
Exponentially Weighted Imitation Learning for Batched Historical Data—0
Co-Imitation Learning without Expert Demonstration—0
FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control—0
Fixing exposure bias with imitation learning needs powerful oracles—0
Generic Oracles for Structured Prediction—0
Flatland-RL : Multi-Agent Reinforcement Learning on Trains—0
FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation—0
Flexible and Efficient Long-Range Planning Through Curious Exploration—0
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation Demonstration and Imitation—0
FlowHFT: Imitation Learning via Flow Matching Policy for Optimal High-Frequency Trading under Diverse Market Conditions—0
FlowOE: Imitation Learning with Flow Policy from Ensemble RL Experts for Optimal Execution under Heston Volatility and Concave Market Impacts—0
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment—0
Goal-Driven Imitation Learning from Observation by Inferring Goal Proximity—0
Exploring the use of deep learning in task-flexible ILC—0
A Training-Free Framework for Precise Mobile Manipulation of Small Everyday Objects—0
Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks—0
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal—0
Exploring the trade off between human driving imitation and safety for traffic simulation—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models—0
Co-Imitation: Learning Design and Behaviour by Imitation—0
Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective—0
A Strong Baseline for Batch Imitation Learning—0
Fully General Online Imitation Learning—0
FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulation—0
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate—0
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