SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 951–1000 of 2122 papers

TitleStatusHype
Behavioral Cloning via Search in Video PreTraining Latent Space—0
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios—0
End-to-End Imitation Learning with Safety Guarantees using Control Barrier FunctionsCode1
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation—0
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation LearningCode1
Model-based trajectory stitching for improved behavioural cloning and its applications—0
Discovering Latent Knowledge in Language Models Without SupervisionCode2
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
Learning Graph Search Heuristics—0
Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible RobotsCode0
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning—0
Learning to Optimize in Model Predictive Control—0
Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations—0
Learning and Blending Robot Hugging Behaviors in Time and Space—0
Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control—0
Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula—0
Multi-Task Imitation Learning for Linear Dynamical Systems—0
Safe Reinforcement Learning with Probabilistic Control Barrier Functions for Ramp Merging—0
Towards Improving Exploration in Self-Imitation Learning using Intrinsic MotivationCode0
Transfer RL via the Undo Maps Formalism—0
A System for Morphology-Task Generalization via Unified Representation and Behavior DistillationCode1
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning—0
imitation: Clean Imitation Learning ImplementationsCode3
Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback—0
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models—0
Dynamic Conditional Imitation Learning for Autonomous DrivingCode1
Follow the Clairvoyant: an Imitation Learning Approach to Optimal ControlCode0
Out-of-Dynamics Imitation Learning from Multimodal DemonstrationsCode0
NeuroCERIL: Robotic Imitation Learning via Hierarchical Cause-Effect Reasoning in Programmable Attractor Neural NetworksCode0
ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning—0
ProtoX: Explaining a Reinforcement Learning Agent via PrototypingCode0
Beyond spiking networks: the computational advantages of dendritic amplification and input segregationCode0
Deconfounding Imitation Learning with Variational InferenceCode0
Learning Modular Robot Locomotion from Demonstrations—0
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning—0
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games—0
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous DrivingCode1
Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative ReasoningCode0
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning—0
PlanT: Explainable Planning Transformers via Object-Level RepresentationsCode2
Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning—0
Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR StreamingCode0
Text Editing as Imitation GameCode0
Differentiable Constrained Imitation Learning for Robot Motion Planning and Control—0
Robust Imitation via Mirror Descent Inverse Reinforcement Learning—0
Learning and Retrieval from Prior Data for Skill-based Imitation Learning—0
NIFT: Neural Interaction Field and Template for Object Manipulation—0
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and ExperimentationCode2
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback—0
Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learning—0
Show:102550
← PrevPage 20 of 43Next →

No leaderboard results yet.