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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 451–500 of 2122 papers

TitleStatusHype
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
DotaMath: Decomposition of Thought with Code Assistance and Self-correction for Mathematical ReasoningCode1
Efficient Fusion and Task Guided Embedding for End-to-end Autonomous Driving—0
Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning—0
Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards—0
Equivariant Diffusion PolicyCode2
Open-TeleVision: Teleoperation with Immersive Active Visual Feedback—0
EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning—0
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents—0
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning—0
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents—0
The State-Action-Reward-State-Action Algorithm in Spatial Prisoner's Dilemma Game—0
RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learning—0
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention—0
Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging—0
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy—0
Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks—0
Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert DemonstrationsCode0
CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics—0
Visually Robust Adversarial Imitation Learning from Videos with Contrastive LearningCode0
Physics-informed Imitative Reinforcement Learning for Real-world Driving—0
Offline Imitation Learning with Model-based Reverse Augmentation—0
An Imitative Reinforcement Learning Framework for Autonomous DogfightCode3
EvIL: Evolution Strategies for Generalisable Imitation LearningCode1
Leveraging Locality to Boost Sample Efficiency in Robotic ManipulationCode1
Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation—0
BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic ManipulationCode0
Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation—0
PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner—0
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving—0
Is Value Learning Really the Main Bottleneck in Offline RL?Code3
OpenVLA: An Open-Source Vision-Language-Action ModelCode9
A Dual Approach to Imitation Learning from Observations with Offline Datasets—0
MaIL: Improving Imitation Learning with MambaCode1
RILe: Reinforced Imitation Learning—0
Online Adaptation for Enhancing Imitation Learning PoliciesCode0
Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion ModelsCode2
Phase-Amplitude Reduction-Based Imitation LearningCode0
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard—0
Behavior-Targeted Attack on Reinforcement Learning with Limited Access to Victim's Policy—0
Aligning Agents like Large Language Models—0
Adversarial Moment-Matching Distillation of Large Language ModelsCode0
RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots—0
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading—0
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning—0
Aligning Language Models with Demonstrated FeedbackCode2
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning DistillationCode0
Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory—0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
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