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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 601–625 of 2122 papers

TitleStatusHype
A Training-Free Framework for Precise Mobile Manipulation of Small Everyday Objects—0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
Fighting Copycat Agents in Behavioral Cloning from Observation Histories—0
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching—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
Fight fire with fire: countering bad shortcuts in imitation learning with good shortcuts—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
Few-Shot Bayesian Imitation Learning with Logical Program Policies—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
Few-Shot In-Context Imitation Learning via Implicit Graph Alignment—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
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