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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 401–425 of 2122 papers

TitleStatusHype
Adapting by Analogy: OOD Generalization of Visuomotor Policies via Functional Correspondence—0
VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections—0
Behavioral Cloning from Noisy Demonstrations—0
A Model-Based Approach to Imitation Learning through Multi-Step Predictions—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning—0
Adapting a World Model for Trajectory Following in a 3D Game—0
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training—0
DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models—0
Task-Agnostic Learning to Accomplish New Tasks—0
DINO Pre-training for Vision-based End-to-end Autonomous Driving—0
Accelerating Training in Pommerman with Imitation and Reinforcement Learning—0
Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning—0
Leveraging Human Guidance for Deep Reinforcement Learning Tasks—0
Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations—0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information—0
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning—0
Discovering hierarchies using Imitation Learning from hierarchy aware policies—0
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning—0
Deep imitation learning for molecular inverse problems—0
BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation—0
Deep Bayesian Reward Learning from Preferences—0
Bayesian Multi-type Mean Field Multi-agent Imitation Learning—0
A Linearly Constrained Nonparametric Framework for Imitation Learning—0
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