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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 1901–1925 of 2122 papers

TitleStatusHype
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
Find a Way Forward: a Language-Guided Semantic Map Navigator—0
Finding Fallen Objects Via Asynchronous Audio-Visual Integration—0
Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback—0
A Versatile Agent for Fast Learning from Human Instructors—0
FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control—0
Fixing exposure bias with imitation learning needs powerful oracles—0
FLARE: Robot Learning with Implicit World Modeling—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
Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer—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
FoldNet: Learning Generalizable Closed-Loop Policy for Garment Folding via Keypoint-Driven Asset and Demonstration Synthesis—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
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
From Motor Control to Team Play in Simulated Humanoid Football—0
From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Fully General Online Imitation Learning—0
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