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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
SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring—0
DIDA: Denoised Imitation Learning based on Domain Adaptation—0
Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid—0
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation—0
Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics—0
Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization—0
Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning PoliciesCode0
LORD: Large Models based Opposite Reward Design for Autonomous Driving—0
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic SimulationCode0
Imitating Cost-Constrained Behaviors in Reinforcement LearningCode0
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations—0
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination—0
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History—0
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling—0
Automated Feature Selection for Inverse Reinforcement Learning—0
Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation, Transferable Reward Recovery and Algebraic Equilibrium ProofCode0
Information-Theoretic Distillation for Reference-less Summarization—0
Augmented Reality Demonstrations for Scalable Robot Imitation Learning—0
Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types—0
What AIs are not Learning (and Why)—0
Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian Motion—0
AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents—0
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight—0
Supervised Fine-Tuning as Inverse Reinforcement Learning—0
VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies—0
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