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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 1051–1100 of 2122 papers

TitleStatusHype
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training—0
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning—0
Discovering hierarchies using Imitation Learning from hierarchy aware policies—0
Discriminator-Guided Model-Based Offline Imitation Learning—0
Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets—0
Dissipative Imitation Learning for Robust Dynamic Output Feedback—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods—0
Distilling Realizable Students from Unrealizable Teachers—0
Distributional Decision Transformer for Hindsight Information Matching—0
Distributionally Robust Imitation Learning—0
Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks—0
DITTO: Offline Imitation Learning with World Models—0
Diverse Imitation Learning via Self-OrganizingGenerative Models—0
Diverse Imitation Learning via Self-Organizing Generative Models—0
Offline Diversity Maximization Under Imitation Constraints—0
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning—0
Divide and Repair: Using Options to Improve Performance of Imitation Learning Against Adversarial Demonstrations—0
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning—0
Document Level Hierarchical Transformer—0
Domain Adaptive Imitation Learning with Visual Observation—0
Reinforcement Routing on Proximity Graph for Efficient Recommendation—0
Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones—0
Relational Mimic for Visual Adversarial Imitation Learning—0
Reparameterized Variational Divergence Minimization for Stable Imitation—0
Repeated Inverse Reinforcement Learning—0
Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization—0
Representation Matters: Offline Pretraining for Sequential Decision Making—0
Reshaping Robot Trajectories Using Natural Language Commands: A Study of Multi-Modal Data Alignment Using Transformers—0
Residual Policy Gradient: A Reward View of KL-regularized Objective—0
Residual Q-Learning: Offline and Online Policy Customization without Value—0
Resolving Copycat Problems in Visual Imitation Learning via Residual Action Prediction—0
Rethink AI-based Power Grid Control: Diving Into Algorithm Design—0
Rethinking Latent Redundancy in Behavior Cloning: An Information Bottleneck Approach for Robot Manipulation—0
Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning—0
Rethinking ValueDice: Does It Really Improve Performance?—0
Rethinking ValueDice: Does It Really Improve Performance?—0
Reward-free Policy Imitation Learning for Conversational Search—0
Reward function shape exploration in adversarial imitation learning: an empirical study—0
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery—0
STIR^2: Reward Relabelling for combined Reinforcement and Imitation Learning on sparse-reward tasks—0
RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot—0
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration—0
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping—0
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation—0
RIFT: Closed-Loop RL Fine-Tuning for Realistic and Controllable Traffic Simulation—0
RILe: Reinforced Imitation Learning—0
Risk-Sensitive Generative Adversarial Imitation Learning—0
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation—0
RLIF: Interactive Imitation Learning as Reinforcement Learning—0
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