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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 1701–1750 of 2122 papers

TitleStatusHype
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances—0
Learning to Multi-Task Learn for Better Neural Machine Translation—0
On Computation and Generalization of Generative Adversarial Imitation Learning—0
The Past and Present of Imitation Learning: A Citation Chain Study—0
Infinite-Horizon Differentiable Model Predictive Control—0
Multi-Agent Interactions Modeling with Correlated PoliciesCode1
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning—0
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate—0
Variational Imitation Learning with Diverse-quality DemonstrationsCode1
Learning to Infer User Interface Attributes from Images—0
Reward-Conditioned PoliciesCode0
Side-Tuning: A Baseline for Network Adaptation via Additive Side NetworksCode1
A New Framework for Query Efficient Active Imitation Learning—0
Hierarchical Variational Imitation Learning of Control ProgramsCode0
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
One-Shot Imitation Filming of Human Motion Videos—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
Relational Mimic for Visual Adversarial Imitation Learning—0
To Follow or not to Follow: Selective Imitation Learning from Observations—0
Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning—0
Learning to Request Guidance in Emergent Communication—0
Deep Bayesian Reward Learning from Preferences—0
Imitation Learning via Off-Policy Distribution MatchingCode1
Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes—0
Learning Norms from Stories: A Prior for Value Aligned Agents—0
Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks—0
Continuous Online Learning and New Insights to Online Imitation Learning—0
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional PoliciesCode0
Compiler Auto-Vectorization with Imitation LearningCode0
Deep imitation learning for molecular inverse problems—0
Compositional Plan VectorsCode0
Learning a Decision Module by Imitating Driver's Control Behaviors—0
Urban Driving with Conditional Imitation Learning—0
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration—0
Neural Random Forest Imitation—0
Meta Adaptation using Importance Weighted Demonstrations—0
State Alignment-based Imitation Learning—0
Third-Person Visual Imitation Learning via Decoupled Hierarchical ControllerCode0
Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning—0
MANGA: Method Agnostic Neural-policy Generalization and Adaptation—0
On Value Discrepancy of Imitation Learning—0
Motion Reasoning for Goal-Based Imitation Learning—0
Accelerating Training in Pommerman with Imitation and Reinforcement Learning—0
A Divergence Minimization Perspective on Imitation Learning MethodsCode1
Learning One-Shot Imitation from Humans without HumansCode0
Learning from Trajectories via Subgoal DiscoveryCode0
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning—0
Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning—0
Positive-Unlabeled Reward Learning—0
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