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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 776–800 of 2122 papers

TitleStatusHype
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning—0
Robotic Constrained Imitation Learning for the Peg Transfer Task in Fundamentals of Laparoscopic Surgery—0
VectorPainter: Advanced Stylized Vector Graphics Synthesis Using Stroke-Style Priors—0
Sub-goal Distillation: A Method to Improve Small Language AgentsCode0
Imitation Learning in Discounted Linear MDPs without exploration assumptions—0
IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning—0
Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling—0
CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning—0
Guiding Attention in End-to-End Driving ModelsCode0
A Survey of Imitation Learning Methods, Environments and Metrics—0
Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World ModelsCode0
Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods—0
Benchmarking Mobile Device Control Agents across Diverse Configurations—0
IDIL: Imitation Learning of Intent-Driven Expert Behavior—0
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots—0
A survey of air combat behavior modeling using machine learning—0
Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories—0
Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent CollaborationCode0
Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model—0
Adversarial Imitation Learning via Boosting—0
AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent—0
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery—0
SAFE-GIL: SAFEty Guided Imitation Learning for Robotic Systems—0
CNN-based Game State Detection for a Foosball Table—0
Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs—0
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