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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 626–650 of 2122 papers

TitleStatusHype
FoldNet: Learning Generalizable Closed-Loop Policy for Garment Folding via Keypoint-Driven Asset and Demonstration Synthesis—0
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal—0
Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning—0
Active Hierarchical Imitation and Reinforcement Learning—0
A survey of air combat behavior modeling using machine learning—0
Combining Imitation and Reinforcement Learning with Free Energy Principle—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
A Study of Imitation Learning Methods for Semantic Role Labeling—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
Extending Multilingual Machine Translation through Imitation Learning—0
Expressive Whole-Body Control for Humanoid Robots—0
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy—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
Extracting Contact and Motion from Manipulation Videos—0
Extracting Relations between Non-Standard Entities using Distant Supervision and Imitation Learning—0
Extraneousness-Aware Imitation Learning—0
Combating False Negatives in Adversarial Imitation Learning—0
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
FABG : End-to-end Imitation Learning for Embodied Affective Human-Robot Interaction—0
Flexible and Efficient Long-Range Planning Through Curious Exploration—0
FlowOE: Imitation Learning with Flow Policy from Ensemble RL Experts for Optimal Execution under Heston Volatility and Concave Market Impacts—0
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