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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 1651–1700 of 2122 papers

TitleStatusHype
Coarse-to-Fine 3D Keyframe Transporter—0
Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration—0
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories—0
CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity—0
Co-Imitation: Learning Design and Behaviour by Imitation—0
Co-Imitation Learning without Expert Demonstration—0
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy—0
Collaborating Action by Action: A Multi-agent LLM Framework for Embodied Reasoning—0
Combating False Negatives in Adversarial Imitation Learning—0
Combining Imitation and Reinforcement Learning with Free Energy Principle—0
Complex Skill Acquisition Through Simple Skill Imitation Learning—0
Complex Skill Acquisition through Simple Skill Imitation Learning—0
Compressed imitation learning—0
Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification—0
ConBaT: Control Barrier Transformer for Safe Policy Learning—0
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint—0
Concurrent Training Improves the Performance of Behavioral Cloning from Observation—0
Conditional Driving from Natural Language Instructions—0
Conditional Imitation Learning for Multi-Agent Games—0
Conditional Kernel Imitation Learning for Continuous State Environments—0
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
Connecting the Dots Between MLE and RL for Sequence Prediction—0
Context-Former: Stitching via Latent Conditioned Sequence Modeling—0
Contextual Bandits and Imitation Learning via Preference-Based Active Queries—0
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning—0
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills—0
Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling—0
Continuous Control with Action Quantization from Demonstrations—0
Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL)—0
Continuous Online Learning and New Insights to Online Imitation Learning—0
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning—0
Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation—0
Convergence of Value Aggregation for Imitation Learning—0
CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics—0
Coordinated Multi-Agent Imitation Learning—0
CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World—0
Correlation Priors for Reinforcement Learning—0
Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study—0
Coupled Distributional Random Expert Distillation for World Model Online Imitation Learning—0
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning—0
CRISP: Curriculum Inducing Primitive Informed Subgoal Prediction for Hierarchical Reinforcement Learning—0
Cross-domain Imitation from Observations—0
Cross Domain Imitation Learning—0
Cross-Domain Imitation Learning with a Dual Structure—0
Cross-Episodic Curriculum for Transformer Agents—0
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
CrowdPlay: Crowdsourcing human demonstration data for offline learning in Atari games—0
CubeDAgger: Improved Robustness of Interactive Imitation Learning without Violation of Dynamic Stability—0
Curating Demonstrations using Online Experience—0
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