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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 1301–1350 of 2122 papers

TitleStatusHype
Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations—0
Learning Solution Manifolds for Control Problems via Energy Minimization—0
Find a Way Forward: a Language-Guided Semantic Map Navigator—0
MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot CommunicationCode0
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy OptimizationCode0
An Adaptive Human Driver Model for Realistic Race Car Simulations—0
Fail-Safe Adversarial Generative Imitation LearningCode0
A Versatile Agent for Fast Learning from Human Instructors—0
LISA: Learning Interpretable Skill Abstractions from LanguageCode0
Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks—0
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories—0
Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections—0
Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer—0
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization—0
RNGDet: Road Network Graph Detection by Transformer in Aerial Images—0
Bayesian Imitation Learning for End-to-End Mobile Manipulation—0
Robust Learning from Observation with Model MisspecificationCode0
Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning—0
Multi-Modal Fusion for Sensorimotor Coordination in Steering Angle PredictionCode0
Memory-based gaze prediction in deep imitation learning for robot manipulation—0
Imitation Learning by State-Only Distribution MatchingCode0
Bayesian Nonparametrics for Offline Skill DiscoveryCode0
A Ranking Game for Imitation Learning—0
Rethinking ValueDice: Does It Really Improve Performance?—0
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning—0
Challenging Common Assumptions in Convex Reinforcement Learning—0
Practical Imitation Learning in the Real World via Task Consistency Loss—0
Yordle: An Efficient Imitation Learning for Branch and Bound—0
A General, Evolution-Inspired Reward Function for Social RoboticsCode0
Adversarial Imitation Learning from Video using a State Observer—0
Robust Imitation Learning from Corrupted Demonstrations—0
Transfering Hierarchical Structure with Dual Meta Imitation Learning—0
Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learningCode0
Reinforcement Routing on Proximity Graph for Efficient Recommendation—0
Ray Based Distributed Autonomous Vehicle Research Platform—0
Rethinking ValueDice: Does It Really Improve Performance?—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets—0
STIR^2: Reward Relabelling for combined Reinforcement and Imitation Learning on sparse-reward tasks—0
Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents—0
Conditional Imitation Learning for Multi-Agent Games—0
Robust Entropy-regularized Markov Decision Processes—0
Stochastic convex optimization for provably efficient apprenticeship learning—0
Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles—0
Amortized Noisy Channel Neural Machine Translation—0
Modeling Strong and Human-Like Gameplay with KL-Regularized Search—0
Learning to Guide and to Be Guided in the Architect-Builder ProblemCode0
Probability Density Estimation Based Imitation Learning—0
Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample EfficiencyCode0
Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation—0
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