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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 751–775 of 2122 papers

TitleStatusHype
A Dual Approach to Imitation Learning from Observations with Offline Datasets—0
RILe: Reinforced Imitation Learning—0
Online Adaptation for Enhancing Imitation Learning PoliciesCode0
Behavior-Targeted Attack on Reinforcement Learning with Limited Access to Victim's Policy—0
Aligning Agents like Large Language Models—0
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard—0
Phase-Amplitude Reduction-Based Imitation LearningCode0
Adversarial Moment-Matching Distillation of Large Language ModelsCode0
RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots—0
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning—0
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading—0
Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning DistillationCode0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory—0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
Vision-and-Language Navigation Generative Pretrained Transformer—0
Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds—0
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees—0
Diffusion-Reward Adversarial Imitation Learning—0
Amortized nonmyopic active search via deep imitation learning—0
Efficient Imitation Learning with Conservative World Models—0
RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts—0
Reducing Risk for Assistive Reinforcement Learning Policies with Diffusion Models—0
Decision Mamba ArchitecturesCode0
ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers—0
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