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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 601–650 of 2122 papers

TitleStatusHype
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models—0
From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation—0
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment—0
Conditional Kernel Imitation Learning for Continuous State Environments—0
Conditional Imitation Learning for Multi-Agent Games—0
Conditional Driving from Natural Language Instructions—0
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games—0
Concurrent Training Improves the Performance of Behavioral Cloning from Observation—0
ASYNCHRONOUS MULTI-AGENT GENERATIVE ADVERSARIAL IMITATION LEARNING—0
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint—0
ConBaT: Control Barrier Transformer for Safe Policy Learning—0
Accelerating Federated Edge Learning via Topology Optimization—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
A Survey on Imitation Learning for Contact-Rich Tasks in Robotics—0
Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification—0
Compressed imitation learning—0
A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning—0
Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks—0
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges—0
Complex Skill Acquisition through Simple Skill Imitation Learning—0
ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning—0
Complex Skill Acquisition Through Simple Skill Imitation Learning—0
Active Imitation Learning from Multiple Non-Deterministic Teachers: Formulation, Challenges, and Algorithms—0
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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