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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 476–500 of 2122 papers

TitleStatusHype
Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation—0
BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic ManipulationCode0
Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation—0
PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner—0
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving—0
Is Value Learning Really the Main Bottleneck in Offline RL?Code3
OpenVLA: An Open-Source Vision-Language-Action ModelCode9
A Dual Approach to Imitation Learning from Observations with Offline Datasets—0
MaIL: Improving Imitation Learning with MambaCode1
RILe: Reinforced Imitation Learning—0
Online Adaptation for Enhancing Imitation Learning PoliciesCode0
Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion ModelsCode2
Phase-Amplitude Reduction-Based Imitation LearningCode0
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard—0
Behavior-Targeted Attack on Reinforcement Learning with Limited Access to Victim's Policy—0
Aligning Agents like Large Language Models—0
Adversarial Moment-Matching Distillation of Large Language ModelsCode0
RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots—0
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading—0
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning—0
Aligning Language Models with Demonstrated FeedbackCode2
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning DistillationCode0
Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory—0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
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