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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 1551–1575 of 2122 papers

TitleStatusHype
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning—0
A Strong Baseline for Batch Imitation Learning—0
Causal Confusion and Reward Misidentification in Preference-Based Reward Learning—0
A Study of Imitation Learning Methods for Semantic Role Labeling—0
A survey of air combat behavior modeling using machine learning—0
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges—0
A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning—0
A Survey on Imitation Learning for Contact-Rich Tasks in Robotics—0
ASYNCHRONOUS MULTI-AGENT GENERATIVE ADVERSARIAL IMITATION LEARNING—0
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games—0
A Training-Free Framework for Precise Mobile Manipulation of Small Everyday Objects—0
Augmented Reality Demonstrations for Scalable Robot Imitation Learning—0
Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories—0
A Unifying Framework for Causal Imitation Learning with Hidden Confounders—0
Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)—0
Auto-Encoding Adversarial Imitation Learning—0
Auto-Encoding Inverse Reinforcement Learning—0
Automated Feature Selection for Inverse Reinforcement Learning—0
Automated Task-Time Interventions to Improve Teamwork using Imitation Learning—0
Automatic Curricula via Expert Demonstrations—0
Automating Deformable Gasket Assembly—0
Autonomous Navigation in Complex Environments—0
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
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