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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 801–825 of 2122 papers

TitleStatusHype
Hierarchical Reinforcement Learning for Multi-agent MOBA Game—0
Data-driven Traffic Simulation: A Comprehensive Review—0
HILONet: Hierarchical Imitation Learning from Non-Aligned Observations—0
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring—0
Hindsight Generative Adversarial Imitation Learning—0
Hindsight is Only 50/50: Unsuitability of MDP based Approximate POMDP Solvers for Multi-resolution Information Gathering—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration—0
How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV—0
Coarse-to-Fine 3D Keyframe Transporter—0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
Imitation Learning from Pixel-Level Demonstrations by HashReward—0
How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers—0
How to Train Your Robots? The Impact of Demonstration Modality on Imitation Learning—0
Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining—0
Human-Agent Cooperation in Bridge Bidding—0
Human AI interaction loop training: New approach for interactive reinforcement learning—0
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback—0
Human-in-the-Loop Imitation Learning using Remote Teleoperation—0
Human-in-the-Loop Task and Motion Planning for Imitation Learning—0
Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors—0
Human-Robot Navigation using Event-based Cameras and Reinforcement Learning—0
Hybrid Adversarial Imitation Learning—0
HybridGen: VLM-Guided Hybrid Planning for Scalable Data Generation of Imitation Learning—0
Adversarial Imitation Learning via Random Search—0
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